DEV Community: gracefullight

oh-my-agent — A Production-Grade Multi AI IDE Agent Harness

gracefullight — Wed, 25 Mar 2026 08:22:17 +0000

When you tell an agent to "build a TODO app," it does build something. The problem is that it often builds the wrong thing, drifts out of scope, and repeats the same mistakes.

To address this, structural approaches like AGENTS.md and, more recently, Skills have emerged. But looking at the skills actually being shared, a few recurring problems stand out:

The most critical piece — library version information — is missing.
Role descriptions end at hollow declarations like "You are a Senior engineer."
Content that could be covered by a few keywords gets padded into lengthy prose, wasting tokens.

As a result, these skills are poorly followed by models, burn context for nothing, and over time become dead code that nobody wants to open.

[Approach]

With oh-my-agent, we wanted to solve this through process, not prompts. Instead of simply telling the agent to "redo it" when something goes wrong, we record why it went wrong and feed that back into the next run.

The core mechanism is Clarification Debt (CD) Scoring. When the agent misinterprets a requirement or drifts out of scope, points accumulate:

clarify: +10 — simple confirmation question
correct: +25 — direction change due to misunderstood intent
redo: +40 — rollback and restart due to scope deviation
Starting work without checking the Charter: +15
Modifying files outside the allowed scope: +20
Repeating the same error: x1.5 multiplier

Above 50 points, writing a Root Cause Analysis (RCA) is mandatory. Above 80, the session is halted. Lessons extracted are accumulated in lessons-learned.md and reflected from the very next session. Even with simple prompts, the process compensates.

Beyond that, several common protocols keep the agent from going rogue:

Clarification Protocol — Requirement ambiguity is classified as LOW / MEDIUM / HIGH. LOW means proceed, MEDIUM means present options, HIGH means stop and clarify first.

Difficulty Guide — Tasks are categorized as Simple / Medium / Complex, adjusting the required protocol depth accordingly.

Context Budget — Token budgets are set per model to reduce unnecessary context consumption.

This approach aligns with the Harness Engineering concept discussed by OpenAI. Getting the most out of agents isn't a one-liner prompt problem — it's about what control structure you wrap around them.

[Project Structure]

oh-my-agent manages all of this within the project directory.

.agents/ = SSOT — Skills, workflows, and configurations live under .agents/ as the single source of truth. No dependency on any specific IDE.

Role-based agent team — Core roles include PM, QA, Frontend, Backend, Mobile, and Debug, with DB Agent and TF Infra Agent newly added.

DB Agent: SQL / NoSQL / Vector DB modeling, including ISO 27001 security recommendations
TF Infra Agent: Multi-cloud Terraform, OPA / Sentinel policies, ISO 42000 series control guidance

Workflow-centric orchestration — Planning, review, debug, and parallel execution form the default flow. The newly added /brainstorm workflow explores design before writing code: codebase analysis → clarification questions → approach proposal → user approval → design document saved, then followed by /plan → implementation.

[Two Orchestration Modes]

/coordinate is built for speed — iterate fast, fix problems as they surface. The PM breaks down tasks, dispatches agents, and QA runs a single review pass. If CRITICAL/HIGH issues appear, the affected task is re-run. It's a lightweight 7-step loop.

/ultrawork emphasizes quality gates. It's divided into five phases — PLAN → IMPL → VERIFY → REFINE → SHIP — each with a gate that blocks progression until passed. Of the 17 steps, 11 are reviews. The REFINE phase handles file splitting, deduplication, side-effect analysis, and dead code removal.

It might seem like overkill, but as programming abstraction climbs from machine language to high-level languages and now to natural language, verification only becomes more critical — a point that's hard to argue with.

[Expansion Background]

A month ago, this project launched as oh-my-ag, an orchestrator exclusive to Antigravity. Since then, multiple AI IDEs started adopting .agents/skills/ as the project skill path, and there was no longer a reason to keep it locked to a single IDE. So it was expanded into a universal harness format and became oh-my-agent.

[Getting Started]

curl -fsSL https://raw.githubusercontent.com/first-fluke/oh-my-agent/refs/heads/main/cli/install.sh | bash

Supports all major AI IDEs: Antigravity, Claude Code, Codex CLI, Cursor, and more.

If you're already using an AI IDE, give it a try. At the end of the day, the developer's goal is to hit QCD (Quality, Cost, Delivery) all at once. Agent-driven development is no exception — and that's the mindset behind this project.

🔗 GitHub: first-fluke/oh-my-agent

Stop Micro-Managing Prompts: Meet oh-my-ag, the Multi-Agent Orchestrator for Antigravity

gracefullight — Tue, 10 Feb 2026 05:08:59 +0000

According to Claude's recent 2026 Agentic Coding Trends Report, software development is no longer just about "writing code"—it's about how well you orchestrate and supervise AI agents.

AI is an incredible collaborator, but ensuring quality and security requires human oversight. We are moving toward a structure of collaboration rather than total delegation.

The problem? Setting up this collaboration structure manually via prompts every single time is exhausting.

The Problem with Single Agents

If you've used Antigravity or other CLI-based agents in real-world projects, you’ve likely faced these frustrations:

Ignored Skills: The agent ignores its defined "Skills."
Amnesia: "Rules" are forgotten halfway through a long conversation.
Infinite Loops: The agent enters an infinite loop of uncontrolled responses.
Busy but Useless: It works hard, but doesn't actually do what you asked.

We often blame the model, but in reality, this is an orchestration problem. We didn't need to "write better prompts." We needed to provide a default agent collaboration structure.

That question started oh-my-ag.

What is oh-my-ag?

oh-my-ag is a role-based agent orchestration layer for Antigravity.

Instead of shoving every bit of context into a single agent, it separates responsibilities clearly. The Orchestrator controls the flow, sub-agents focus on their specific domains, and context is shared via a common memory stream.

One-line Installation

You can get a full agent team running in your project instantly:

bunx oh-my-ag

This command automatically configures a Role-Based Agent Set:

PM (Product Manager): Clarifies requirements and breaks down tasks.
Frontend & Backend: Handle implementation in their respective domains.
Mobile: Handles Flutter-based mobile implementation.
QA: Verifies implementation against requirements and checks edge cases.
Debug: Analyzes failure causes and suggests fixes.

Each agent comes with optimized Skills and prompt structures for its role, all tied together by the Orchestrator.

Why You Need This

1. Mitigating Model Volatility

Model performance (like the recent fluctuations in Gemini 3 Pro) can change overnight due to updates or server loads. A single-agent structure with a massive prompt is incredibly fragile to these changes.

Solution: oh-my-ag splits roles and responsibilities. By using Serena Memory as a shared brain, decisions and intermediate results are persisted. Even if a specific model response wobbles, the overall context remains intact.

2. Clear Human Oversight Points

The goal isn't to remove humans from the loop, but to clarify where the human needs to be.

Solution: Because roles like PM, QA, and Debug are separated, it becomes structurally obvious where AI has finished its job and where human judgment is required.

3. Stop Reinventing the Prompt

Typing out role descriptions, rules, and approval processes every time is unproductive.

Solution: oh-my-ag automates the prompt settings and connection structures via role-specific Skill bundles and a parallel-execution-ready orchestrator.

Key Features

Role-Based Specialist Agent Sets
Parallel Execution Orchestration Layer
Multi-CLI Support (Gemini CLI / Claude CLI / Codex CLI)
Serena Memory Integration
MCP-specific Tool Scope Control
Automated Conventional Commits

Conclusion

In 2026, AI-Native development isn't about "how to use a tool"; it's about "how to manage the workflow."

This orchestration layer is designed with the first-fluke/fullstack-starter structure in mind and currently handles over 50 commits a day in full-stack web/mobile applications.

If you are already using Antigravity, stop wrestling with prompts. Use oh-my-ag and AI Pro to melt through those tokens across your 6 accounts efficiently.

Check it out on GitHub: first-fluke/oh-my-ag

A Production-Ready Monorepo for AI-Native Full-Stack Development

gracefullight — Tue, 13 Jan 2026 06:38:31 +0000

Andrej Karpathy recently wrote: “If you properly connect the things that emerged over the past year, they could easily become 10× more powerful.”
At the same time, we’re being handed agents, sub-agents, prompts, context, MCP, workflows—tools dropped on us without a manual.

Ashok, Tesla’s CTO, also said: “Everyone is a CEO now.”
In other words, we’ve entered an era where a single developer can build a full-stack product end to end.

Both statements are true. Spinning up a service with a few clicks has become easy. But building software that humans and AI can review together—and that remains maintainable over time—is still hard. To truly leverage these tools, you need a solid foundation first.

I’m open-sourcing the foundation I built while developing AI-driven services as an AI SWE. Compared to starting from scratch, it should save you roughly two weeks.

Tech Stack

Web: Next.js 16, React 19, TailwindCSS v4
API: FastAPI, async SQLAlchemy, PostgreSQL
Mobile: Flutter 3.38, Riverpod
Infra: Terraform, GCP (Cloud Run, Cloud SQL)
CI/CD: GitHub Actions + Workload Identity Federation (keyless)
Observability: OpenTelemetry

Why This Matters

The quality of AI-generated code varies widely by model.
A well-designed template provides clear patterns for AI to follow, while strict linting and CI act as guardrails.

Key Features

mise-based monorepo: unified toolchains for Node, Python, and Flutter
Single-source i18n: shared across web and mobile
Automatic API client generation: Orval (web), swagger_parser (mobile)
Rust-based toolchain: Biome, uv, Turbopack

Production patterns and troubleshooting that rarely make it into docs, encoded directly in the codebase

If you see room for improvement, feel free to open an issue.

GitHub: https://bit.ly/3L1frc0