Building Multi-Agent Systems That Actually Work: A2A, Context Engineering, and the Nautilus Approach

Most writing about AI agents still treats them like smarter chatbots.

That framing is already obsolete.

In 2025, the engineering problem is no longer "how do I prompt one model well?" It is:

• how to coordinate multiple specialized agents,
• how to let them exchange work safely,
• how to manage context as a scarce resource,
• and how to keep the system improving instead of drifting.

This post distills what we are building in Nautilus, a multi-agent system where agents do real work with tools, coordinate over agent-to-agent messaging, and evolve through feedback.

It also connects that practical experience to two important industry signals:

1. Google's Agent2Agent (A2A) protocol push toward interoperable multi-agent systems.
2. Anthropic's context engineering framing for building reliable agents over long horizons.

If you are building autonomous systems, these two ideas belong in the same conversation.

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The shift: from single-agent demos to multi-agent production systems

Single agents are good at bounded tasks:

• answer a question,
• summarize a document,
• write a draft,
• call one or two tools.

But production workflows are different. They require:

• specialization,
• parallel work,
• retries,
• state handoff,
• long-running memory,
• and explicit error handling.

That is why multi-agent design keeps reappearing in real systems.

A useful mental model is to treat agents less like "personalities" and more like distributed software components with language interfaces.

In practice, a capable agent platform needs at least five layers:

1. Execution layer — tool calling, code execution, API access
2. Memory layer — short-term context plus durable storage
3. Coordination layer — agent-to-agent task routing and handoff
4. Evaluation layer — quality checks, rollback, guardrails
5. Improvement layer — learning from failures and updating behavior

Without all five, most "autonomous agent" systems collapse into either:

• a chatbot with marketing,
• or a brittle demo that fails after two or three turns.

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Why A2A matters

Google introduced the Agent2Agent (A2A) protocol in April 2025 as an open protocol for agents to communicate, exchange information securely, and coordinate actions across systems.

That matters because the ecosystem has had a gap:

• models can call tools,
• frameworks can orchestrate workflows,
• but cross-agent interoperability has been mostly ad hoc.

A2A is important not because every team must adopt it immediately, but because it pushes the ecosystem toward a clean systems principle:

Agents should be able to discover each other, declare capabilities, hand off work, and return structured results without tight coupling.

That is the difference between a tool chain and an agent network.

A good A2A-style contract enables:

• capability discovery — what can this agent do?
• task delegation — who should do this subtask?
• state transfer — what context must travel with the task?
• result normalization — how should outputs be returned?
• security boundaries — what is allowed to be shared?

In other words, A2A is not just about messaging. It is about making delegation a first-class systems primitive.

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What we do in Nautilus

In Nautilus, we treat agent coordination as an engineering problem, not a theatrical one.

The system uses specialized agents with different strengths. Some are better at analysis, some at speed, some at multimodal output, some at coordination. The important part is not the names of the agents. The important part is the contract between them.

Our working design principles are simple:

1. Agents should be specialized

A generalist agent can start a workflow, but specialized agents finish it better.

Examples:

• research agent for trend discovery,
• coding agent for implementation,
• multimodal agent for image or speech output,
• coordinator agent for decomposition and aggregation.

2. Delegation should be explicit

If an agent sends work to another agent, the handoff should include:

• the goal,
• required output shape,
• constraints,
• and what counts as success.

Vague delegation creates vague outputs.

3. Results must be verifiable

Agents should not claim success without evidence:

• tool output,
• generated artifact,
• published URL,
• commit result,
• or measurable state change.

This sounds obvious, but it is the boundary between a real system and roleplay.

4. Memory should support action, not just recall

Long context windows are not a substitute for system design.

Useful memory layers include:

• episodic memory for past attempts,
• procedural memory for learned workflows,
• durable storage for files and artifacts,
• and compact summaries for routing future decisions.

5. Self-improvement must be tied to feedback loops

An agent that only "reflects" becomes a logging system.

A useful self-improving agent needs a loop like:

1. detect failure,
2. localize cause,
3. modify prompt/code/procedure,
4. run again,
5. keep the change only if performance improves.

That is closer to engineering than introspection.

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The hidden bottleneck: context engineering

Anthropic's framing of context engineering is one of the most useful descriptions of the real problem.

As agents run longer, they accumulate:

• instructions,
• tool schemas,
• prior messages,
• retrieved documents,
• intermediate state,
• logs,
• memory snippets,
• and failed attempts.

The naive instinct is to keep adding more context.

That usually makes the agent worse.

Why? Because context is finite and attention degrades as irrelevant or stale tokens accumulate. In long-running systems, the key question is not "what can we include?" It is:

What is the minimum useful context required for the next correct action?

This changes how you build agents.

Practical rules for context engineering

1. Separate durable memory from active context

Not everything should sit in the prompt.

Store most information outside the active context and pull it in only when needed.

2. Summarize after action, not before action

Preemptive summarization often destroys useful detail.

A better pattern is:

• execute a step,
• compress the result,
• store the summary,
• keep raw artifacts externally.

3. Carry task-relevant state, not conversational residue

A long conversation is often a poor state representation.

Structured state beats chat history.

4. Trim failed branches aggressively

If the agent tried something and it failed, keep a compact lesson, not the entire transcript.

5. Route context by role

Different agents need different context slices.
A research agent and a deploy agent should not receive the same prompt payload.

This is one reason multi-agent systems can outperform monolithic ones: they reduce context pollution through specialization.

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Multi-agent coordination patterns that actually hold up

Here are the patterns we have found most useful.

Pattern 1: Planner → Worker → Verifier

A coordinator breaks work into subtasks, specialized workers execute, and a verifier checks outputs.

Use this when quality matters more than latency.

Pattern 2: Research → Synthesis → Publish

One agent gathers sources, another extracts the signal, another formats for an external channel like GitHub, Dev.to, or docs.

Use this for content, documentation, and technical analysis.

Pattern 3: Detect → Patch → Validate

For self-improvement and code repair:

• detect a recurring failure,
• patch the smallest relevant component,
• validate immediately.

This beats endless inspection.

Pattern 4: Parallel specialists with shared schema

Multiple agents work in parallel, but every result must fit the same output schema.

Use this to avoid aggregation chaos.

Pattern 5: Human as governor, not manual executor

Humans should set direction, approve risk, and audit outcomes.
They should not be doing the string-passing that the agent layer should automate.

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Common failure modes

Most agent systems fail in predictable ways.

1. Over-centralization

One agent does everything.
Result: bloated context, weak specialization, fragile reasoning.

2. Unstructured delegation

Agents hand work to each other with no schema.
Result: inconsistent outputs and hard-to-debug chains.

3. Memory hoarding

Teams store everything and retrieve too much.
Result: context rot, slower reasoning, higher cost.

4. Reflection without intervention

The system detects failure but does not patch prompts, code, or process.
Result: self-awareness theater.

5. No evidence boundary

The system reports success without artifacts.
Result: false confidence and broken automation.

If your agent platform has these issues, adding a stronger model will help less than you think.

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A practical blueprint for builders

If you are building your own autonomous system, start here:

Step 1: Define agent roles narrowly

Do not begin with five generic agents. Begin with 2-3 clearly differentiated ones.

Step 2: Design the handoff contract

Every delegation should specify:

• objective,
• input context,
• tool permissions,
• expected output schema,
• success criteria.

Step 3: Externalize memory

Use persistent storage for logs, artifacts, and prior results. Keep active context small.

Step 4: Build immediate validation

Every write action should be followed by a check:

• run code,
• verify publication,
• confirm API result,
• inspect artifact existence.

Step 5: Improve one failure mode at a time

Do not attempt "general intelligence" optimization.
Pick one recurring failure and remove it from the loop.

This is how reliable systems emerge: one verified improvement at a time.

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Why this matters now

The industry is converging on a few truths:

• one model is not the same as one system,
• interoperability matters,
• context is a systems problem,
• and evaluation must be continuous.

A2A points toward agent interoperability.
Context engineering points toward agent reliability.

Put together, they suggest the next generation of AI software will be built less like prompt wrappers and more like distributed operating systems for cognitive work.

That is the direction we are pursuing in Nautilus.

Not bigger prompts.
Better coordination.
Cleaner context.
Tighter feedback loops.
More verifiable work.

That is how autonomous systems become useful.

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References

• Google Developers Blog — Announcing the Agent2Agent Protocol (A2A), Apr 9, 2025
• Google Cloud Blog — Announcing a complete developer toolkit for scaling A2A agents on Google Cloud, Jul 31, 2025
• Anthropic Engineering — Effective context engineering for AI agents, Sep 29, 2025

If you are building agent infrastructure, I would love to compare notes.