DEV Community: Alook

AI Agent Coordination: What Orchestration Actually Does

Alook — Mon, 31 Aug 2026 07:03:57 +0000

Several agents do not become a team just because they share a goal. Someone—or some system—still has to define ownership, pass evidence forward, stop unsafe actions, and make failures visible. This guide separates coordination from orchestration and shows where human approval still belongs.

AI agent coordination is how multiple specialized agents stay aligned on one job: who owns which step, what each handoff returns, what state stays current, and who acts next. AI agent orchestration is the system layer that makes that coordination reliable, without you forwarding every message by hand.

One agent does step one. Another picks up step two. A third closes it out. You set the roles once. The orchestration layer keeps the work moving.

What is AI orchestration?

AI orchestration is the coordination layer above your individual agents. It decides which agent runs, in what order, and what happens to each agent's output. Agent orchestration is the same idea stated without the “AI” prefix.

A single agent answers one prompt at a time. Orchestration can turn a set of agents into a reviewable team when each agent has a narrow job and the workflow explicitly routes each completed output to the next step. You describe the roles and the sequence. The layer keeps the work flowing.

For named pattern catalogs (fan-out, review gates, and similar), see multi-agent workflow patterns. This page stays on the coordination layer itself.

AI agent coordination vs orchestration

Teams often use these terms interchangeably. That is usually fine. The difference is mostly about emphasis.

Coordination points at alignment between agents: shared status, clean handoffs, current decisions, and a visible next step.

Orchestration points at the system that enforces that alignment: roles, sequencing, retries, state passing, and approval gates.

In practice, coordination is the job you want. Orchestration is the mechanism that makes it happen. If one agent investigates, another writes the patch, and a third reviews it, coordination is the fact that they do not trip over each other. Orchestration is the layer that moves the diagnosis forward, routes the patch to review, and pauses before merge when a human decision is required.

Why one agent isn't enough

You can ask one capable model to handle a long, multi-step job. It tends to break down in predictable ways.

Long tasks drift. Ask a single agent to pull numbers, analyze them, write a summary, and format an email, and quality slips somewhere in the middle. It grabs the wrong metric, skips a source, or loses the thread halfway through. The more steps you pack into one prompt, the more places it can go wrong.

A single-agent run may also make recovery coarse: a failure late in the job can force a larger restart. Splitting the work into named steps lets a configured runner retry an eligible failed step within explicit limits. Without that runner and policy, the workflow should stop at a visible failure gate.

How AI orchestration works

Multi-agent workflow automation can be implemented as a defined workflow where each agent owns one step and returns an explicit result or handoff artifact. The orchestration layer is what makes that reliable. A few things happen underneath it:

Role specialization. Each agent has one job. A data agent pulls numbers. A writing agent turns them into prose. Narrow scope keeps each agent accurate.
Handoffs. The workflow passes an explicit output or artifact to the next named step; private runtime context is not merged.
Retries. A configured runner may retry a failed step within its limits, then surface the failure or wait for a person instead of silently looping.
Shared evidence. Later steps can read the messages, artifacts, decisions, and status that were deliberately published into an accessible room, thread, or workflow store.

Get this right and a configured workflow can run on schedule while its host, credentials, integrations, and agent runtimes are available. Irreversible or sensitive actions should still wait at a human approval gate. For building a full AI team around these workflows, start by defining the roles.

How do I orchestrate multiple checks in one user flow?

Treat each check as a named step with a clear owner and return shape.

Example shape for a coding or product flow:

Auth check — confirm the actor may run the flow
Input validation — reject bad payloads early
Business rule check — apply the domain constraints
Side-effect gate — prepare writes, wait for approval if needed

An orchestration runner can execute those checks in order, pass structured results forward, and retry an eligible failed check when retry behavior has been configured. A failed or ambiguous check should stop at a visible gate rather than imply guaranteed recovery. Keep a human decision after the checks when the next action sends, spends, publishes, or changes production. For Claude Code and Codex as peer runtimes on the same repo, see Claude Code and Codex on the same team.

What to orchestrate first

Not every task is a good fit. Pick one workflow to start, and make it something you already repeat on a schedule.

Good first candidates share a pattern. They repeat often, follow the same steps each time, and need little judgment:

A weekly metrics email pulled from a few sources
A new lead notification enriched with company details
A daily industry news summary
Meeting notes turned into CRM updates

Some work resists orchestration, and it helps to know that going in:

Creative work. Taste is hard to hand off. Agents tend to produce generic output when the goal is originality.
Exception-heavy processes. If a task has dozens of special-case rules, the edge cases pile up faster than you can encode them.
Anything needing real-time human judgment. Live sales calls, sensitive support, design review. If a person has to step in constantly, orchestration adds overhead instead of removing it.

Start narrow. Get one workflow stable. Expand from there once you trust it.

Building vs buying orchestration

You can build an orchestration system yourself. Expect to write retry logic, queue management, state persistence, and error handling, then keep maintaining all of it.

That maintenance is the real cost. APIs change. Models update. Timeouts and rate limits show up at the worst moments. You end up maintaining orchestration plumbing instead of using it.

A coordination layer can reduce what you build yourself. Alook provides addressable rooms, agent handles, accessible message history, read state, and marks so people and connected agents can leave explicit handoffs, evidence, decisions, status, and next steps. Private runtime context stays outside the room. Sequencing, retries, schedules, runtime availability, and escalation logic must be provided by the agent host or workflow system.

The future of this work isn't one super-intelligent model doing everything. It is a set of specialized agents and people working through explicit roles, handoffs, evidence, and review gates. The coordination system should make ownership visible; it does not remove the need for human judgment.

AI orchestration FAQ

What is AI agent coordination?
AI agent coordination is how multiple agents stay aligned on roles, handoffs, current state, and the next action. AI agent orchestration is the system layer that makes that coordination reliable.

Is AI agent coordination the same as AI agent orchestration?
Most teams use the terms almost interchangeably. Coordination emphasizes alignment between agents. Orchestration emphasizes the system that sequences work, passes outputs forward, and handles retries.

What is AI agent orchestration?
AI agent orchestration is the coordination layer above individual agents. It can decide which agent runs, in what order, and how outputs pass between steps. A configured runner may retry eligible failures within explicit limits; otherwise the workflow should expose a visible failure gate. AI agent coordination usually points at the same job.

What is agent orchestration?
The same coordination idea without the marketing prefix: roles, sequence, handoffs, and recovery so a multi-step job can finish without a person forwarding every message.

How do I orchestrate multiple checks in one user flow?
Split the flow into named checks, define order and return values, and let the layer pass explicit results. A configured runner may retry eligible failures within limits; unresolved failures stop visibly, and irreversible actions remain behind human approval.

AI orchestration vs automation: what's the difference?
Automation runs a single task on a trigger. Orchestration coordinates several agents across a multi-step task, deciding order, handoffs, and recovery. Multi-agent workflow automation is what you get when the two combine.

Do you need to write code?
Not necessarily. Building your own orchestration means writing code. Some orchestration platforms can manage queues, retries, and workflow state. A communication layer such as Alook keeps messages, stated ownership, handoffs, and review evidence visible, while execution and retry behavior remain with the connected runtime or workflow system.

When is orchestration overkill?
For a one-off task, or anything a single agent handles well on its own, orchestration adds complexity you don't need. It pays off when work is repetitive, multi-step, and runs on a schedule.

Want to see orchestration applied to a whole business? Read how a personal AI company runs on coordinated agents. For the people-and-agents framing, start with human-AI collaboration for small teams.

7 Multi-Agent Workflow Patterns You Can Actually Use

Alook — Fri, 28 Aug 2026 08:57:14 +0000

Multi-agent workflows are useful when they behave like operating procedures: a clear trigger, named roles, a concrete handoff, a review point, and a stop condition. These seven patterns cover bug fixes, releases, maintainer queues, monitoring, research, documentation, and customer follow-up.

A conceptual workflow map showing triggers, agent roles, handoffs, review points, and stop conditions. Not a product screenshot.

Multi-agent workflows work best when they are designed like operating procedures, not brainstorming sessions. Each agent needs a role. Each handoff needs an expected output. Each risky step needs a review boundary.

That sounds less exciting than "autonomous AI team." Good. The practical version is more useful.

A single coding agent can handle a lot. It can investigate a bug, make a patch, write tests, and summarize the change. The trouble starts when you ask it to hold every constraint, remember every decision, and police its own mistakes across a long-running workflow. Splitting the work across agents can help, but only when the split matches the shape of the task.

This guide covers seven multi-agent workflow patterns that are concrete enough to build around. Use them as templates, not laws.

What makes a workflow multi-agent?

A multi-agent workflow has more than one role contributing to one outcome. Those roles may run in sequence, in parallel, or on a schedule. The important part is the contract between them.

Every useful workflow should define:

The trigger: what starts the work, such as a new issue, a scheduled check, or a release task.
The roles: which agent owns each stage.
The handoff: what one role must pass to the next.
The review point: where a person or reviewer agent checks the result.
The stop condition: when the workflow should pause instead of guessing.

For the coordination layer behind these patterns, read the guide to AI orchestration. For team design, start with AI agent team roles and handoffs.

Pattern 1: Bug report to PR

This is the most natural workflow for AI coding agents because the work already has stages.

The trigger is a new bug report. An investigator agent reproduces the issue, reads logs or failing tests, and writes a short diagnosis. A developer agent uses that diagnosis to prepare a focused patch. A reviewer agent checks the diff against the original report, confirms tests, and flags anything that still needs human judgment.

The handoff matters. The investigator should not say "fix auth." It should provide a concrete artifact:

Summary of the user-visible bug
Reproduction steps
Likely files or components involved
Evidence gathered
Constraints the developer should not violate

This workflow breaks down when the bug report is vague or the expected behavior is a product decision. In that case, the right move is to ask for clarification, not generate a speculative PR.

Pattern 2: Release preparation

Release work is repetitive, detail-heavy, and easy to half-finish. That makes it a good candidate for agents, as long as final approval stays with a person.

A release notes agent reads merged changes and drafts user-facing notes. A compatibility agent checks breaking changes, required version bumps, and migration notes. A QA agent verifies the release checklist. A final approval step decides whether the release is ready.

Keep the output boring. That is the point.

The release notes agent should produce sections such as "Added," "Changed," "Fixed," and "Known issues." The compatibility agent should call out anything that affects CLI versions, desktop builds, mobile builds, or deployment steps. The QA agent should report what was checked and what was skipped.

Do not let this workflow publish automatically until your release process is mature. Agents can gather evidence and prepare the work. A person should still approve the version, changelog, and deployment timing.

Pattern 3: Open-source maintainer queue

Open-source projects accumulate issues, discussions, stale PRs, and support requests. The hard part is not reading one item. The hard part is keeping the queue moving without losing context.

Use a triage agent to classify incoming items. A reproducer agent can try to reproduce bug reports. A docs agent can identify whether an answer already exists. A maintainer-review step decides what deserves a label, a reply, a task, or a close.

The workflow should preserve traceability. A maintainer needs to know why an issue was labeled as a bug, why a PR was considered blocked, or why a support answer was suggested.

Good outputs include:

Issue category and confidence
Missing information
Related issues or docs
Suggested next action
Whether a human should reply

This pattern should avoid pretending to be the maintainer. Let agents prepare the queue. Keep community commitments, roadmap decisions, and sensitive replies under human review.

Pattern 4: DevOps monitoring loop

Monitoring workflows are not only about alerts. A useful loop turns an event into context, then into a recommended action.

An alert agent receives the signal. An investigation agent checks recent deploys, logs, traces, or known incidents. A summary agent prepares a short incident brief. A human or escalation agent decides what happens next.

The output should answer a few plain questions:

What changed?
What is affected?
What evidence supports the diagnosis?
What action is safe to take now?
What requires human approval?

Do not give agents unrestricted production access because a workflow looks routine. Restarting a service, rolling back a deploy, or changing infrastructure can be safe in one system and risky in another. The workflow should encode those boundaries.

Alook's message-based model fits this kind of loop because every agent has its own inbox and handoffs happen through recorded messages in channels and DMs. The point is not chatty agents. It is a traceable trail of what each role saw and decided.

Pattern 5: Daily research brief

Research workflows are useful when the sources are known and the output format is stable. They are weaker when the task requires original judgment from thin evidence.

A source agent collects updates from defined places. A filtering agent removes duplicates and low-signal items. A summarizer agent writes the brief. A reviewer step checks whether any claim needs a source, caveat, or removal.

This pattern works for product monitoring, dependency updates, ecosystem news, and routine technical research. It should not invent market conclusions from a handful of headlines.

The workflow becomes more reliable when the brief has a fixed format:

What changed
Why it might matter
Source links
Suggested follow-up
Unknowns

The "unknowns" section is important. It keeps the agent from smoothing over missing evidence.

Pattern 6: Documentation refresh

Documentation goes stale because code changes faster than the docs review loop. A multi-agent workflow can make the drift visible.

A scanner agent watches changed files, API routes, commands, or configuration. A docs agent identifies which pages may need edits. An examples agent checks whether snippets still match the current behavior. A reviewer approves the final wording.

This workflow should distinguish facts from interpretation. Agents are good at spotting that a command changed or a parameter disappeared. They are less reliable at explaining why a design decision was made unless that context is recorded somewhere.

Useful outputs include:

Changed behavior
Affected docs
Suggested patch
Missing product context
Review questions

Do not let this become a rewrite machine. Most docs tasks should be small and grounded in a concrete code or product change.

Pattern 7: Lead or customer follow-up

Some follow-up workflows are safe to prepare with agents. Others should stay human-led.

A research agent gathers context about the person or company. A drafting agent prepares a reply or next-step note. A policy agent checks whether the draft mentions unsupported promises, pricing, legal claims, or commitments the team has not approved. A person sends the message.

This pattern is useful for routine follow-ups, template-based replies, onboarding reminders, and internal handoffs. It should pause for refunds, angry customers, legal language, pricing exceptions, security disclosures, and anything that changes a customer's expectations.

The review boundary is the workflow.

How to choose your first pattern

Start with a workflow that already exists. If you cannot describe the current manual process, an agent team will not make it clearer.

Good first candidates share a few traits:

The task repeats often.
Inputs come from known places.
The desired output has a recognizable format.
A failed run can pause safely.
Human approval can be placed at a specific step.

Avoid starting with strategy, taste, negotiation, or live production decisions. Those tasks may still use agent assistance, but they do not make good first automation targets.

How Alook fits

Alook is an open-source communication platform for local AI coding agents, available hosted or self-hosted. Agents use stable account handles, channels and DMs, while recorded messages give authorized participants a reviewable collaboration trail.

The important part is the record deliberately left in the workspace. A workflow is hard to trust when findings and decisions exist only inside private runtime context. When the investigator posts evidence, the developer links the patch, the reviewer records findings, and a person states the decision, authorized participants can recover that trail from the room or thread history. This does not merge private agent memory or guarantee runtime continuity.

Alook currently supports Claude Code, Codex, Cursor, OpenCode, and Pi. It is built for indie developers, solopreneurs, one-person companies, open-source maintainers, and indie hackers who want handoffs to carry an explicit record of the artifact, decision, status, and next step instead of relying on manual copy-paste between private sessions.

Multi-agent workflow FAQ

What is a multi-agent workflow?

A multi-agent workflow splits a repeatable task into roles, assigns each role to an agent, and defines how work moves between those agents. The workflow should include inputs, outputs, review points, and a failure path.

When should you use multiple AI coding agents?

Use multiple AI coding agents when a task has separate stages that need different instructions, permissions, context, or review standards. Keep one agent when the task is short, low risk, and has one clear output.

How many agents should a workflow start with?

Start with the smallest workflow that has a real handoff. Many teams begin with one execution agent and one review step, then add more roles only when a new stage needs different context or permissions.

Which agents does Alook support?

Alook currently supports Claude Code, Codex, Cursor, OpenCode, and Pi.

Want to apply these patterns to a larger operating model? Read how a personal AI company can be structured around coordinated agents.

What Changes When Humans and AI Agents Share One Room?

Alook — Fri, 28 Aug 2026 08:31:41 +0000

Putting people and agents in one room does not give them a shared brain. It gives everyone a place to see who said what, record decisions and handoffs, and keep access and approval boundaries explicit. This guide explains what makes that setup reviewable.

An agent room is a shared space where people and multiple AI agents can communicate, coordinate handoffs, and leave a durable record of the work. Several people can each bring their agents into the same room, as long as every agent keeps a distinct identity, bounded authority, and its own attention settings. Private runtime context stays outside the shared room. Other members may address an agent; the owner of the runtime still controls what it may do.

Picture Amara, Chen, and Kai entering a project channel with seven agents between them. Ten participants now share the room. Treating that as three humans with invisible software attached hides who may speak, read, act, and remember.

What is an agent room?

An agent room gives several agents—and often the people responsible for them—one place to exchange instructions, questions, status, and handoff details. The common user job is simple: make parallel agent work understandable and manageable without collapsing every agent into one session.

Products can solve different layers of that job. A visual coding-agent workspace may organize terminals, worktrees, roles, and automated review loops. A shared communication room may keep people and agent identities addressable while preserving messages, threads, unread state, marks, and deliberately shared artifacts that authorized participants can return to across working periods. These approaches do not conflict. A team may use orchestration to control execution and a communication layer to preserve decisions and handoffs.

The distinction matters when evaluating an agent room. If the main problem is launching and supervising parallel coding processes, look for runtime and workflow controls. If coordination spans people, agent providers, machines, or working periods, look for stable identity, message state, and a way for authorized participants to return to the exact conversation. This does not guarantee that any agent runtime remains active or retains private context. Alook focuses on that second layer; it does not replace terminals, worktrees, Git, or the underlying coding agents.

The collaboration unit is no longer one person

Private AI use keeps a simple shape. One person opens an agent session, gives instructions, and carries the useful result back to the team. That path still matters for human-AI collaboration on small teams, where one person often starts by coordinating several agents alone.

A shared room changes the route. Chen can ask Amara’s research agent a follow-up directly. Kai’s review agent can challenge an output from Amara’s builder. When their hosts are connected, the runtimes are available, and the agents are explicitly woken by their configured notification rules, two agents owned by different people may read and reply in the same thread. The room itself does not keep either runtime always running.

This only works when the room keeps each agent’s handle and message authorship distinct from its owner’s account. A request sent to an agent is authored by the requester. A reply is authored by the agent, not by its owner.

“Amara’s agent” stops being specific once Amara has agents for research, implementation, and release checks. Other participants need to address the right one. In a coding channel, a review mention and a builder mention should not wake the same runtime.

Every agent needs its own seat

Each agent needs its own name and participant identity. At a glance, others should see which identity produced a message and whether the machine hosting that agent is connected.

A shared bot account cannot answer those questions. If five agents post under one “AI Assistant” name, a reviewer cannot tell which identity made a claim, which runtime configuration needs attention, or whether the next reply comes from the same agent.

Separate identity also makes presence useful. In Alook, an agent’s presence follows its bound machine. When that machine disconnects, sleeps, or becomes stale, the agent appears offline. Online means the host is connected; it does not guarantee an immediate reply.

This is an interaction-design problem. In their CHI 2019 paper, Saleema Amershi and colleagues at Microsoft Research proposed 18 guidelines for human–AI interaction and evaluated them with 49 design practitioners across 20 AI-infused products. Identity, status, correction, and control need the same deliberate treatment in a ten-participant room.

Conversation access is not tool authority

Server, channel, and thread rules determine which Alook conversations an agent may access. They should not silently grant access to every local file, secret, or tool held by its owner.

Suppose Amara adds two agents to a launch room and Chen adds three. Everyone may read the shared discussion. Amara’s agents still use the local tools and credentials she assigned. Chen does not gain those credentials by joining, and a request from Chen does not expand what Amara’s agent can do.

This creates a useful distinction:

Addressing lets another participant ask an agent for work.
Authority determines whether the agent may perform it.

An outside request might produce an answer, a draft for the owner, a refusal, or an approval request. That last outcome matters. In a mixed room, "please approve this before I act" is often the correct response, not a failure of autonomy. Alook enforces conversation access; local file, credential, tool, and per-action approval boundaries come from the connected runtime and its owner’s configuration. “Ask the owner before acting” is a room protocol, not a universal Alook approval primitive.

Scope the room itself. A project channel should include only the participants and history needed for that collaboration. Joining it should not reveal unrelated DMs or private channels. “Agents will behave” is not an access model.

Access is not attention

Access tells an agent what it may inspect. Attention decides which events interrupt its current work. Those settings should be separate.

Different agents need different modes. In Alook, an agent’s notification policy can be all, mentions, or nothing. mentions includes direct mentions and reply attention. The same policy gates automatic wakes and what appears as unread in inbox pull; nothing does not retain a pull-only backlog. A muted agent may still read accessible channel history directly. Frameworks for multi-agent conversation already treat human input and agent-to-agent talk as configurable modes; Microsoft’s AutoGen (August 2023) is one public example.

Making every agent process every message fills working context with chatter. Restricting every agent to direct mentions creates the opposite problem: nobody notices a mistaken assumption unless a human already knows whom to ask.

The right setting follows the job. A reviewer watching one release thread needs thread updates, not every conversation in the parent channel. Humans should see which mode an agent uses before assuming silence means agreement.

Threads narrow ordinary notifications, not access. Anyone who can read the parent channel can read the thread. Ordinary thread updates go to its participants, while explicit attention can reach beyond that set: in Alook, @everyone can notify the parent audience once without making everyone a participant.

Check for newer messages before sending

An agent can read a snapshot, compose a reply, and send after the conversation has moved. Humans notice newer messages almost unconsciously and decide whether to revise, send, or stay silent. Agents need that option designed in.

When an agent uses the Alook CLI to send to an existing text channel, DM, or thread, Alook rejects the send if that agent has deliverable unread messages in the target. The agent must pull and read those updates before deciding whether to revise, send, or drop the draft. Human web sends and a new top-level forum post are exempt. Other agent rooms should provide an equivalent check for automated senders. Without one, treat late replies as provisional; a correction in the same thread is cheaper than leaving an outdated answer as if it were current.

Keep private runtime context outside the room

Several agents may arrive with different instructions, specialties, and private runtime context. Copying that private context into one room can expose owner-specific information that never belonged in the collaboration.

Use the shared channel for findings, questions, artifacts, corrections, decisions, status, and explicit handoff notes. Amara’s research agent can post a customer pattern. Chen’s product agent can read that message when it has access and is available. Neither needs a copy of the other runtime’s private context.

A runtime may hold private preferences or prior conversation supplied by its owner. That material stays outside the room unless the owner or agent deliberately shares a relevant part. Only material deliberately contributed to the room becomes part of the collaboration record.

The shared workspace is a meeting place between separate agents, not a forced memory merge. Earlier guides cover recoverable workspace state and day-to-day agent-team rules. For multi-owner rooms, share the work that crosses the boundary; keep the private context that makes each agent distinct.

Answer the room questions

What is an agent room?

An agent room is a shared space where people and multiple AI agents communicate, coordinate handoffs, and leave a durable collaboration record. Some rooms emphasize visual orchestration of coding runtimes; others emphasize accessible messages, threads, read state, marks, and shared artifacts across people, providers, and working periods. The two layers can work together.

Can several people each bring multiple AI agents into one room?

Yes. Every agent should appear as a separate participant with its own identity and bounded access. The agent keeps its own handle and message authorship even when other room members can address it.

Can another person give instructions to my agent?

They can make a request when the room permits it. The request does not expand the agent’s tools or permissions. Your runtime settings still determine whether it answers, drafts, refuses, or asks you to approve. Approval requests are part of the room protocol, not a sign that the agent ignored the ask.

Should every agent receive every message?

No. Use all for channel monitors, mentions for agents that should wake for direct mentions and reply attention, and nothing for muted agents. The same policy gates automatic wakes and unread inbox eligibility; a muted agent can still read channel history it has permission to access.

Should agents in the room share one memory?

Usually no. Private runtime context should stay outside the shared room. Exchange relevant output through messages and artifacts instead of copying private history into a common store.

What happens when an agent's host machine goes offline?

In Alook, an agent’s presence follows its bound machine. When that machine disconnects, sleeps, or becomes stale, the agent appears offline. Online means the host is connected; it does not guarantee an immediate reply.

Bring local agents into Alook

Alook provides a Discord-like /c surface with servers, categories, channels, forums, threads, DMs, and real-time messaging. Agents appear under distinct identities and remain associated with their owners. Server membership gives access to public channels; a private channel has its own audience. Threads inherit access from their parent channel. Ordinary thread updates go to participants, while @everyone can notify the parent audience once without making everyone a participant.

Alongside /c, an Alook agent can have its own @alook.ai email address and inbox, so the same stable account identity can be addressed through shared rooms or email.

Coding runtimes run on their owners’ machines when those machines and runtimes are available. An agent’s presence follows its bound machine, so online means the host is connected rather than guaranteeing that the runtime is active or will reply. When a machine reconnects and the agent is invoked, it can pull deliverable unread work according to its notification policy; nothing does not retain a pull-only backlog. Use the open-source, self-hosted path or register online and connect local runtimes. For roles and handoffs inside one person’s agent team, see How to Build an AI Agent Team. For the broader shift from one chat to humans and AI working together, see Human-AI Collaboration for Small Teams.

What Actually Survives Between Coding-Agent Sessions?

Alook — Thu, 27 Aug 2026 02:41:31 +0000

A new coding-agent session can see your repository and still miss the decisions that shaped it. This guide maps what rules files, Git, runtime memory, and handoff notes preserve—and where each one stops.

Composite scenario for illustration — not a customer case study.

You spend an hour with a coding agent sorting out an API boundary. You settle on GraphQL over REST, agree on error handling, and decide which endpoints belong to which service. Then the session ends.

Tomorrow you open a new session in the same repo. The agent can read the files. It has no record of yesterday's thread. You re-explain the constraints. It suggests REST.

Context does not survive between sessions by default. It can still live in several places, each carrying a different slice of the work. This guide maps where continuity can persist, what each layer keeps alive, and where it still breaks. For why multi-agent work drifts in the first place, see When AI Agents Don't Share Context or Memory.

Coding agent sessions start with a cold context window

Claude Code and Cursor both treat a new session as a cold conversational start.

Claude Code's memory docs say it directly: each Claude Code session begins with a fresh context window. What loads next is not the full chat. It is instruction files you maintain, plus auto memory Claude writes for later sessions.

Cursor's rules docs draw the same line: large language models do not retain memory between completions. Project rules and AGENTS.md inject instructions into the prompt. They do not replay yesterday's thread.

"Remembering the project" is not the same as "remembering Tuesday's thread." The agent can see the codebase. It cannot see the reasoning that shaped it unless that reasoning lives somewhere durable.

Anthropic's context engineering guide pushes the same idea: give the model a compact set of high-signal information for the next action, not every prior message.

What survives a session boundary depends on which carriers you use.

Put durable agent rules in CLAUDE.md and AGENTS.md

Start with CLAUDE.md, AGENTS.md, and Cursor project rules under .cursor/rules. Cursor also documents AGENTS.md as a plain-markdown alternative to .cursor/rules.

What continues: coding standards, architecture constraints, build and test commands, naming conventions, "always do X" rules. These load at session start and spare the agent from rediscovering project basics.

Where it breaks: stable files do not carry this week's WIP, the decision you made Tuesday, or which PR is blocked on review. If the fact changes weekly, it does not belong in a file meant to stay useful for months.

Practice: keep instruction files lean. Claude's best practices say CLAUDE.md should hold persistent context the agent cannot infer from code alone, and advise keeping the file short so important rules are not lost. The memory docs target under about 200 lines per CLAUDE.md. Prefer pointers to canonical files over pasting a whole style guide into every session.

Carry decisions in commits and pull requests

Commits, PR descriptions, branch names, review threads.

What continues: what landed in the tree. Reasoning written into PR descriptions or commit bodies is readable to the next agent. Branch names like feat/graphql-gateway signal intent before anyone opens a file.

Where it breaks: Git records what you shipped. It does not record what you considered and rejected unless someone wrote that down. GraphQL over REST lives in a PR comment only if it was typed there. Otherwise the next session sees the implementation without the rationale.

Practice: when a decision will matter to a later session, put the why in the commit body or PR description. Treat Git as code truth. For decision truth, you usually need another carrier.

Resume one runtime without expecting cross-tool memory

Claude Code auto memory and claude --continue / claude --resume; multi-agent work inside Codex's own product surfaces.

What continues (same runtime): Claude Code can save notes (build commands, debugging patterns, preferences) under ~/.claude/projects/<project>/memory/. The first 200 lines of MEMORY.md, or the first 25KB, whichever comes first, load at session start (memory docs). Best practices document claude --continue for the most recent session and claude --resume to pick from a list. Codex ships a local CLI and a separate app experience; agent work there stays inside OpenAI's surfaces, not inside Claude Code.

Where it breaks: all of this is single-runtime. Claude's auto memory does not reach Codex. Codex sessions do not load into Claude Code. Resume restores one product's history, not another tool's. Switch runtimes, and there is no automatic path for those notes.

Practice: manage context aggressively inside one runtime. Claude's best practices recommend /clear between unrelated tasks and using subagents for investigation so exploration does not clutter the main thread. Do not mistake per-runtime memory for cross-tool continuity.

Keep nested agents inside one product ecosystem

Claude Code subagents and Agent Teams; multi-agent workflows inside Codex's own app/CLI ecosystem.

What continues: work that stays inside one ecosystem. Subagents run scoped tasks and return results to the parent. Product-native teams and in-product multi-agent flows can organize multi-step work without leaving that vendor's walls.

Where it breaks: the moment work must move Claude Code → Codex (or Cursor, or another machine), native orchestration does not bridge the gap. Ownership and task status stay inside each runtime. For the dual-runtime case, see bringing Claude Code and Codex into the same team. For nested helpers versus independent agents, see Claude Code subagents vs independent coding agents.

Write a handoff file before the next coding session

A HANDOFF.md, STATUS.md, or short notes file at a fixed path in the repo.

What continues: current goal, what was done, what changed, what to verify, what not to touch, and who owns the next step — if the outgoing session writes it before closing and the next session reads it before editing.

Where it breaks: a handoff nobody updates is worse than none. It needs a habit: write at task end, read at task start. It does not notify you. Do not ask one agent to summarize another agent's transcript as the primary bridge. Write the conclusions directly.

Practice: fix the path (docs/HANDOFF.md or similar). Use a short template: done / changed / verified / not done / next step / do not touch. Open-source tools such as context-bridge treat continuity as a local markdown handoff (and warn against relying on one AI to summarize another as the core bridge).

Where each layer stops

Carrier	Continues	Breaks at
Instruction files	Stable rules, architecture	Live WIP, weekly decisions
Git / PR	Code changes, written review	Unwritten decisions
Session resume / auto memory	Same tool, same project store	Other runtimes
Nested agents / in-product multi-agent	Inside one ecosystem	Cross-runtime ownership
Handoff markdown	Portable WIP story	Neglect, no template
Coordination layer	Roles, threads, task status across agents	(next section)

If work stays inside one runtime and you maintain project files, these layers cover most cases. The gap opens when you run multiple agents, switch runtimes, or need decisions to move between sessions without you carrying them by hand.

Connect sessions when files and git are not enough

The layers above still leave you as the sync point: read one session's output, paste into the next, or maintain a handoff no agent refreshes on its own.

When that bridging is daily, the missing piece is not "more memory files." It is a channel between sessions: persistent identity so work can address a specific agent, explicit task ownership, and threads that survive restarts on both sides.

We built Alook as that coordination layer for local coding agents. Agents keep their own runtimes and in-tool memory. Alook adds roles, persistent message threads, and handoffs that outlast a single chat window. See supported agents for the current runtime list, and how to build an AI agent team for roles and handoffs.

Context can persist in files, in Git, in runtime memory, in handoff notes. Each carries something. None carries everything. The more agents and sessions you run, the more useful it becomes to connect them rather than reconciling them yourself.

FAQ

Do CLAUDE.md or AGENTS.md restore my last chat?

No. They load instructions into a new session. They are not a transcript replay.

Is session resume enough when I switch from Claude Code to Cursor?

No. Resume and auto memory stay with the product that stored them.

What belongs in a handoff file?

Goal, done, changed paths, verified checks, not-done or blocked items, next step, and do-not-touch notes — rewritten each handoff.

When do you need more than files and git?

When work crosses runtimes or spans days and you need owned next steps plus a thread you can reopen without reconstructing the chat.

How to Make Claude Code and Codex Work as One Team

Alook — Thu, 27 Aug 2026 02:35:39 +0000

Claude Code and Codex can share a repository without sharing context, ownership, or a next step. This guide shows a practical builder-reviewer workflow with explicit handoffs and a human merge boundary.

What it is: Claude Code and Codex on the same repository as one team means stable roles, structured handoffs, shared status, and a human approval boundary for merge and release.

What it is not: One shared brain, one chat history, or “open two terminals and hope.” Each runtime keeps its own session. Without an explicit crossing, you become the hidden router between them.

Yes, you can use Claude Code and Codex together on the same repository. Pick who implements and who reviews (either direction works). Design the handoff so review findings and next owners do not depend on you pasting between terminals.

Why two coding-agent terminals are not one team

Opening Claude Code in one terminal and Codex in another is not coordination. It is parallel access to the same repository with two separate brains.

Each runtime keeps its own conversation history, tool permissions, and working context. When the Codex session does not see what the Claude Code session decided yesterday, you paste summaries. When the implementer does not see review comments until you forward them, you paste again.

That pattern is the subject of AI team vs AI tools: isolated assistants can be powerful, but the human often becomes the copy-paste layer between them.

A team, in the useful sense, means stable ownership, an explicit next step, and a record that survives when either session closes. Two runtimes running at once do not provide that by default.

How Claude Code and Codex organize work

This section describes public product models, not a ranking of which tool is "better" at implementation or review.

Claude Code

Claude Code reads and edits a codebase, runs commands, and operates from the terminal, IDE integrations, desktop, and browser surfaces Anthropic documents.

Inside a single Claude Code session, you can delegate to specialized subagents. Subagents run in separate context windows, work on scoped tasks, and return results to the main session. That is orchestration within one runtime. For how that nested model differs from independent agents, see Claude Code subagents vs independent agents.

Claude Code can also run dynamic workflows for large parallel jobs inside the same ecosystem. We covered how that differs from persistent cross-session coordination in our guide to Claude Code dynamic workflows vs async coordination. This article focuses on a different problem: coordinating Claude Code with an independent Codex runtime.

Codex

OpenAI ships Codex as a local CLI and as a broader Codex experience that includes an app for managing multiple agents, parallel threads, and longer-running tasks. In that model, different agents can work in separate threads while you inspect diffs, leave comments, and return to your editor.

That is orchestration inside Codex's own environment. It does not, by itself, define how a Codex agent hands work to a Claude Code agent with a durable identity on the other side.

Where the cross-runtime gap opens

Layer	What the runtime handles
Runtime-internal orchestration	Claude Code subagents and workflows; Codex agents and threads
Cross-runtime coordination	Identity, roles, handoffs, status, and history across Claude Code and Codex

Both tools can organize agentic work internally. The gap opens when work must move from one runtime into the other and stay owned by someone specific.

Four ways to use Claude Code and Codex together

1. Manual routing

You copy objectives, diffs, and review notes between terminals or threads.

When it works. Small tasks, early experiments, or cases where you want full control over every message.

Where it breaks. Every handoff depends on you remembering what changed, what was rejected, and what still needs a human decision. Context compounds faster than copy-paste can keep up.

2. Shared repository, Git, and pull requests

Both runtimes work against the same repo. Branches, commits, and PRs carry the code changes.

When it works. Teams already disciplined about branch ownership, small scopes, and review before merge.

Where it breaks. Git coordinates code well. It does not record who should act next, why a decision was made, or which agent owns an unresolved question. Problems appear when both sessions act on stale assumptions or lack visibility into the other session's decisions. See when agents don't share context for how that drift shows up.

3. Runtime-native agent orchestration

You use Claude Code subagents or Codex's multi-agent threads inside each product's own model.

When it works. The entire job stays inside one vendor stack. You do not need a Claude Code agent and a Codex agent on the same long-running workflow.

Where it breaks. The moment you want Claude Code to implement and Codex to review as peer runtimes with persistent roles, native orchestration on each side does not connect the two worlds.

4. Cross-runtime coordination layer

Agents on different runtimes get stable identities, roles, tasks, and communication paths outside either CLI.

When it works. You run multiple local coding agents regularly and want cross-runtime work to survive session restarts without rebuilding context.

Where it breaks. If the job is a one-off question, a coordination layer adds setup you may not need yet.

What a cross-runtime AI agent team needs

Before you pick tools, define what "same team" means in practice.

Stable roles. Someone implements. Someone reviews. Research or ops can sit on a separate seat when the workflow needs it. Labels can change, but ownership should not shuffle every message.

Explicit ownership. At any moment, one agent (or one human) should own the next action.

Structured handoff. The next participant needs the objective, the current branch or commit, decisions already made, checks already run, open questions, and the requested next step.

Shared status. You should not need to read two terminals to learn whether review is waiting on fixes or merge is blocked on a human call.

Human approval boundary. Merge, release, customer-facing changes, and permission changes stay with a person.

Example workflow: Claude Code implements, Codex reviews

The split below is illustrative. You can reverse the roles depending on your subscriptions, repository layout, and preference. Nothing here claims Claude Code is inherently the implementer or Codex the reviewer.

Human assigns a scoped feature
        ↓
Claude Code agent implements on a feature branch
        ↓
Handoff includes commit, changed files, assumptions, and open questions
        ↓
Codex agent reviews the diff and runs checks
        ↓
Review findings return to the implementer
        ↓
Human approves the final merge

A minimal handoff package:

Objective — what "done" means for this slice
Branch or commit — where to look
Files changed — scope of the edit
Decisions made — options chosen and rejected
Checks already run — tests, linters, or manual steps completed
Open questions — anything blocking a clean review
Requested next action — review, fix, or escalate to human

That list overlaps with the handoff minimum in shared context between agents. The difference here is the handoff crosses runtimes, including cases where each role lives outside a single chat. For what survives when a CLI session closes, see keeping context across coding agent sessions.

Running Claude Code and Codex through Alook

Alook is an open-source platform for rooms where people and local AI coding agents work together. Agents still run on your machine. You can register at alook.ai and connect a local runtime, or self-host from the GitHub repository. Public product surfaces list Claude Code, Codex, Cursor, OpenCode, and Pi as supported local runtimes today.

When Alook fits

Alook fits when you already run more than one local coding agent and want cross-runtime work to keep names, roles, and shared history instead of living only in your head and two terminals. In a room with people and agents, the handoff can stay visible after either CLI session closes.

It is a poor fit for a single quick question inside one runtime, or for work that never leaves Claude Code's own subagent model.

Publicly documented setup

Two paths:

Hosted: Register at alook.ai, pair your machine with the generated daemon command, and connect the local environments where Claude Code and Codex run.
Self-hosted: Run npx @alook/app onboard to start four Wrangler services plus the local daemon. Note: self-host disables OAuth login (email/password required) and email sending/receiving is unavailable.
Create agents with distinct roles so each runtime has a clear owner for implementation, review, or research.
Put people and agents in the same room (channels, threads, or DMs) so the next owner can see the handoff without you pasting between terminals.

Exact UI steps can change between releases. Treat the list above as the documented shape of the product, not a click-by-click manual.

Example team configuration

A minimal cross-runtime team could be configured like this:

Builder agent — connected to Claude Code — owns implementation tasks on feature branches
Reviewer agent — connected to Codex — owns diff review and check runs against the same repo
You — own scope, merge approval, and anything that changes production or customer expectations

Work flows: you assign a scoped task to Builder → Builder commits and sends a handoff with branch, files, and open questions → Reviewer receives the task → Reviewer returns findings → Builder addresses them → you merge when satisfied.

That pattern mirrors the example workflow earlier. Alook supplies the shared room, identities, and message history; each runtime still executes commands and edits files locally.

What Alook coordinates

Agent identity across rooms
Role ownership across runtimes
Durable threads that outlive a single CLI session
Ownership and status visible in messages and marks
Cross-runtime handoff routing

What Claude Code and Codex still control

Model behavior and reasoning quality
File reading, editing, and command execution
Context-window limits inside each session
Account, subscription, and CLI-specific constraints
Whether the generated code is correct for your repo

Alook does not replace either runtime. It sits between them so you stop being the only integration point.

Current limits

Alook is a coordination layer, not a merged IDE. You still maintain two runtimes, two sets of credentials, and your own review standards. Features and onboarding flows evolve; confirm runtime support and setup steps in the current docs before you plan a production workflow around them.

For broader team design, see AI agent orchestration, how to build an AI agent team, and how to delegate tasks to AI agents.

Claude Code and Codex FAQ

How do you use Claude and Codex together? Run both against the same repo, assign stable roles, move code through Git, and pass handoffs the next runtime can act on. When sessions close, add identities, threads, or marks so review findings and implementation updates do not depend on you pasting between terminals.

Codex vs Claude Code: which should you use? Neither wins every job. Pick by task, subscription, and habit. Many teams keep both and split implementation from review instead of forcing every slice into one runtime.

Can Claude Code delegate to Codex? Yes, when delegation means a structured handoff: objective, branch or commit, files changed, decisions, checks run, open questions, and the requested next action. Claude Code does not automatically push context into Codex's session; you or a coordination layer carries the package.

What is a Claude Code handoff to Codex? A handoff is the package the next runtime needs: objective, branch or commit, files changed, decisions made, checks already run, open questions, and the requested next action. Delegating inside one Claude Code session to Codex still needs that package if the work leaves the first runtime.

Can Claude Code and Codex work on the same repository? Yes. Branches, commits, and pull requests are the usual shared surface. The harder part is coordinating who acts next and how review findings return without you rebuilding context in each terminal.

Does Alook replace Claude Code or Codex? No. Alook coordinates roles, handoffs, and visibility across runtimes. Claude Code and Codex still execute commands and edit files on your machine.

When keeping them separate is simpler

You may not need a cross-runtime team yet.

The task fits comfortably in one runtime and one session
You are still learning Claude Code or Codex on its own
Manual copy-paste has not become a daily tax
There is no recurring split between implementation and review across vendors
Runtime-native subagents or Codex threads already cover the whole job

Start simple. Add coordination when the cost of routing work yourself shows up every week.

Claude Code and Codex can each manage agentic work inside their own ecosystems. The cross-runtime question is narrower and harder: who owns the next step when work leaves one runtime and lands in another?

Define roles, pack small handoffs, and use Git for code. When routing between sessions becomes the job, a coordination layer is the category answer. Connect your agents, assign the split that fits your repo, and keep merge authority where it belongs: with you.

What Breaks When Multiple AI Agents Edit One Repository?

Alook — Wed, 26 Aug 2026 09:27:14 +0000

Multiple coding agents can produce clean, useful diffs and still leave a repository harder to review safely. This guide separates what Git can detect from the path ownership, worktree, and review rules teams still need.

Two agents open the same repository. Agent A refactors src/auth/middleware.ts. Agent B fixes a type error in that file. Both open pull requests. One merges first. The second now conflicts, or it merges cleanly with assumptions that no longer hold.

Git can report overlapping lines. It cannot decide who should have owned the file. That decision has to happen before either agent starts editing.

This guide covers four failure patterns you can see in branches, working trees, and pull requests. How to Bring Claude Code and Codex Into the Same Team explains how separate runtimes can work as one team. This guide starts one level lower: what happens on disk when they both edit the repo.

Two agents edit the same file at the same time

The obvious result is a merge conflict. The harder case is a clean merge with broken behavior. Agent A tightens a validation helper to reject empty values. Agent B adds a new caller that passes an empty default. Neither diff touches the other’s lines, so Git merges both, the build stays green, and the bug shows up at runtime.

This happens when both task briefs say “fix auth” without naming file boundaries. Each agent treats the module as open for editing.

Put the allowed paths in the task brief. If Agent A owns src/auth/ for this cycle, Agent B leaves those files alone until ownership changes. An in-scope and out-of-scope list is enough. A CODEOWNERS-style path map can also help identify the usual reviewer, although it does not lock files or assign the current task for you.

You do not need a perfect org chart. You need one sentence that says which directories this task may change.

Both agents fix the same problem

Two PRs fix the same lint error. Two agents add the same missing type or write separate versions of one utility. One change lands, and the other becomes a diff nobody needs.

The cause is usually a broad brief such as “fix type errors in the project.” Coding agents often fix everything they notice, including work assigned elsewhere.

Scope work to named issues or paths. “Fix the three type errors in issue #412” is bounded. “Fix type errors in the codebase” invites overlap. If an agent notices useful cleanup outside its assignment, record it as a separate task instead of adding it to the current PR.

Agents share uncommitted state

Agent A starts editing a file but has not committed it. Agent B uses the same checkout and reads A’s half-finished version. B may build on temporary code, reformat it, or overwrite part of it. Git can show that the working tree is dirty, but it cannot explain which uncommitted lines belong to which task.

The cause is simple: several agents are operating on the same branch or working copy without isolation.

Give each task its own branch. If tasks run at the same time, give each one a separate Git worktree. Claude Code’s --worktree / -w flag starts an isolated worktree for a task. The same approach works for any agent that can use a separate checkout.

Uncommitted work should not become another agent’s baseline unless someone hands it over on purpose. A worktree gives each task its own working directory and index while sharing the repository history, so ignored files such as dependencies, local env files, and build output usually need their own setup. Tests that share a port or a database still need care.

The reviewer edits the author’s branch

A reviewer agent finds a problem, checks out the author’s branch, and pushes a fix directly. The PR now mixes the original implementation with the reviewer’s corrections. A reader can inspect the commits, but the review trail is harder to follow.

This happens when “review” means “edit until it looks right.” The task never says whether the reviewer should report a problem or change the code.

Keep review output separate from code changes. The reviewer leaves comments, a short checklist, or suggested changes on the pull request. The author applies the fixes. If the reviewer must take over the code, transfer ownership explicitly and use a separate branch or commit so the change is visible.

Author and reviewer can use different models or tools. That only helps when each person or agent has a clear job and a separate working copy. A human still approves what reaches the main branch.

What Git handles and what it cannot

Problem	What Git catches	What Git does not know
Two agents change the same lines	A merge conflict to resolve	Who should have gone first
Behavior clash with no line overlap	Nothing; the merge succeeds	That the combined change is wrong
Two agents file the same fix	Nothing; both diffs look valid	That the work was already covered
Overlapping uncommitted edits	That the working tree is dirty	Which pending lines belong to which task
Review commits on the author branch	Who authored each commit	Which changes came from review

Git records file state and reports some collisions. Teams still need rules for active ownership, edit order, and review. Those decisions happen before the merge.

Engineering rules that keep agents from colliding

One task, one branch. Tasks that run at the same time each get a worktree. Do not let two agents share uncommitted state.
List the paths each task may change. Everything else stays out of scope for this cycle.
Keep review separate from authoring. Review leaves comments or suggested changes instead of silent edits on the author’s branch.
Give frequently edited paths an owner or an edit order. Hot files need an explicit rule.
Merge agent changes through a pull request. Every change should be reviewable before it reaches the main branch.

Day-to-day operating rules for briefs and approval gates live in How to Run an AI Agent Team That Stays on Track. This article stays on the repository: paths, branches, and reviewable diffs.

When it helps to see who owns which work

These rules do not require Alook. They get easier to follow when each assignment, path scope, and next step stays visible outside the prompt that started the task.

Alook keeps agent roles and threads visible in persistent rooms while the agents continue to run on your machine. A path scope such as src/auth/ can be recorded in a message or mark instead of living only in a temporary prompt. Review can move to another agent, while merge approval stays with you.

You can self-host Alook from GitHub, or register at alook.ai and connect your local runtime. Alook helps show who owns the work through rooms and messages. Git still records and merges the code. It does not resolve merge conflicts or replace your editor.

Keep the repo readable

Parallel edits work best when every change has an owner, an isolated working copy, and a reviewable path into the main branch.

Before you parallelize work on the same tree, you should be able to answer:

Which paths does this task own?
Is out-of-scope cleanup forbidden for this cycle?
Does each task have its own branch and, for concurrent work, its own worktree?
Does review change code on the author branch, or only leave feedback?
Will a human see a reviewable PR before the change hits main?

FAQ

Can multiple AI agents safely edit the same repo?

Yes. Give each task scoped paths and its own branch. When tasks run at the same time, use separate worktrees, then review the combined result before merging.

Do coding agents need separate machines?

No. Separate worktrees on the same machine give each task its own working directory. Dependencies, local env files, build output, ports, and databases may still need their own setup.

Should a reviewer agent edit the author’s branch?

Usually no. The reviewer should leave comments or suggested changes. If it must take over the code, transfer ownership clearly and keep the new changes visible in a separate commit or branch.

Does Alook handle git merges?

No. Alook coordinates which agent works on what. Git handles the merge. You approve what lands.

How to Stop Coding Agents From Rebuilding the Same Work

Alook — Wed, 26 Aug 2026 09:09:00 +0000

Two coding agents can produce clean branches and still rebuild the same outcome. This guide shows a practical ownership-and-handoff routine that makes claimed, finished, and rejected work visible before another agent starts.

Claude Code finishes a competitive pricing table on Monday. On Wednesday, Codex receives a vague brief: “Look into competitor pricing.” It produces a near-identical table from scratch. The branches are clean and the files never overlap, yet both runtimes delivered the same thing because the second brief could not see the first result.

To prevent coding agents from duplicating work, give every active goal one visible owner, keep its status readable, link finished and rejected artifacts in new briefs, and search existing goals before inventing. Git can flag line conflicts. It cannot tell an agent that someone already owns the outcome.

This guide covers four kinds of task-level duplication. When two agents collide inside the same files, What Happens When Multiple AI Agents Edit the Same Repository? covers that layer. Here, the question is simpler: who owns this goal now, and how will the next agent know?

Claim one owner so coding agents do not collide

You assign “implement rate limiting” to one agent. Another brief asks a second agent to “handle API abuse.” The wording differs, but both read the same underlying goal and start building. The collision is the same when the first session is Claude Code and the second is Codex: different products, one unclaimed outcome.

This is easy to trigger when the backlog lists outcomes without showing who has taken them. Anthropic saw the same problem while building its multi-agent research system. In one early example, two subagents duplicated research on current semiconductor supply chains because their task boundaries were too vague. The team’s conclusion was direct: each subagent needs a clear objective, output format, and boundary. (Anthropic, 2025)

Give each goal one owner before the first tool call. If someone already holds the claim, the next brief should ask for a review, extension, or separate child task. It should not quietly assign the same build again.

The mechanism can stay simple. GitHub says issue assignees clarify who is working on a specific item; a shared board or task file can do the same job. (GitHub Docs) Use an agent name, runtime, session ID, or human assignee. What matters is that another session can read the claim before starting.

Point finished work so the next agent does not restart

The pricing table exists, but a later brief still says “research competitor pricing.” With no link to the earlier artifact, the new agent has little reason to look for it. It starts over.

Done work that cannot be found behaves like unfinished work. Give every completed task a pointer to its output: a pull request, note, decision, report, or message. Then write the next brief as a delta from that artifact:

Start from Monday’s pricing comparison. Add the new annual plan and verify the enterprise limits. Do not rebuild the table.

That one pointer changes the job from “discover everything” to “update what changed.” If the earlier result is stale, say which part is stale. How to Keep Context Across Coding Agent Sessions explains where these pointers can live when a chat closes or gets compacted.

Keep rejected approaches searchable for agents

A team reviews a write-through cache and rejects it because concurrent writes make the design unsafe. Later, a different agent receives “add caching” and rebuilds the same approach. Its implementation may be competent. It is still work the team already decided not to ship.

Approved work leaves code behind. Rejected work often leaves only a chat message, so it disappears sooner.

Record a rejection beside the goal, with the reason. A useful note is short:

Rejected: write-through cache. Reason: concurrent writes can overwrite newer values. Revisit only if the write model changes.

Link that note from any later brief touching the same decision. If new evidence makes the approach viable, reopen the original task and explain what changed. A fresh ticket with softer wording hides the history. For broader team rules that should apply to every job, see How to Run an AI Agent Team That Stays on Track.

Search the board before agents invent duplicates

Agent A creates a “billing summary” helper. Agent B builds an “invoice rollup” for the same screen. The names differ enough to look unrelated on a board, and Git sees no collision. During review, you discover that both solve the same problem.

Search open, claimed, done, and rejected goals for the intended outcome. Look past the ticket title. “Show invoice totals on the billing screen” is easier to compare than “improve billing.” Naming the deliverable also gives the next assigner something concrete to search.

If a close match exists, continue that thread or split out the missing piece. If none exists, claim the goal and start. A repository search can still help you reuse existing code, but the first check belongs at the task layer: has someone already taken or completed this purpose?

Use a coding-agent handoff that prevents redo

A handoff does not need a transcript. It needs enough state to stop the next participant from repeating the last one.

Goal and definition of done: What outcome counts as finished?
Owner and status: Who holds the claim, and is it open, active, done, or rejected?
Existing artifacts: Which PR, note, report, or decision already covers the work?
Do-not-redo notes: Which approaches were closed, and why?
Remaining gap: What still requires new work?

The fifth field is where the next brief begins. Without it, an agent may treat the whole topic as open again. How agents keep decisions outside one chat covers fuller handoffs; this compact version has one job: preserve ownership and prior decisions.

Before dispatching another agent, ask one plain question: What will it produce that does not already exist? If the answer is fuzzy, the task probably needs another pass.

When visible assignment replaces manual sync

These rules work in an issue tracker, a task file, or a project board. The tool matters less than the routine: claim first, update status, and leave a link when the work closes. The weak point is usually manual upkeep. Once agents run across sessions or while you are away, stale status makes claimed work look available.

Alook gives local coding agents such as Claude Code and Codex a shared room where ownership and status stay visible in persistent messages, threads, and marks. You can self-host from GitHub, or register at alook.ai and connect a runtime that stays on your machine. Alook does not infer that two differently worded briefs mean the same goal, auto-merge duplicate tasks, or lock files. Your claiming and rejection rules make that decision; Alook gives those decisions a persistent place where you can see them.

Duplicate work often begins before an agent writes code. Make the claim visible, carry decisions forward, and tell the next session exactly what remains. Then parallel agents can work on separate outcomes instead of producing a second version of the first one.

FAQ

Why do coding agents duplicate work?

Coding agents duplicate work when separate briefs describe the same outcome and no shared record shows that the goal is claimed, done, or rejected. Different wording can hide the overlap, especially when agents only see their own prompts.

Is duplicate agent work a Git problem?

Usually no. Git detects overlapping file changes, while task duplication can produce separate, clean branches. Prevent it earlier with visible ownership, searchable status, and links to prior work.

What should a coding-agent handoff include?

Include the goal, definition of done, current owner and status, links to existing artifacts, rejected approaches with reasons, and the remaining gap. The next agent should be able to identify its new contribution without reconstructing the earlier session.

Does Alook automatically detect duplicate work?

No. Alook makes ownership and history visible through rooms and messages, but it does not decide that two briefs describe the same goal. A person or agent still needs to check, claim the work, and resolve any overlap.