Claude Code Agents, Managed at Scale

Stop babysitting AI. chAIrman hires, assigns, monitors, and retires autonomous Claude Code agents that write production-grade code without constant hand-holding.

What are Claude Code agents?

A Claude Code agent is a fully autonomous instance of Anthropic's Claude that operates inside your project directory. Unlike copilots that suggest the next line, agents take ownership of entire tasks: reading your codebase, planning changes, writing code across multiple files, running tests, and committing results.

Each agent runs as an independent Claude Code process with its own context window, file access, and shell permissions. It doesn't wait for you to accept suggestions line by line. You describe the outcome you want, and the agent figures out the implementation.

  • Agents read your existing code before making changes, maintaining consistency with your patterns and conventions
  • Each agent works in isolation with defined file boundaries, so parallel agents never create merge conflicts
  • Agents produce structured handoff documents when finished, making it easy to verify work or hand off to the next agent
// Hire a Claude Code agent hire_agent({ project: "my-saas-app", role: "Backend Engineer", job_description: ` Build the REST API for user auth. Tech: Node.js, Express, PostgreSQL. Files: src/routes/auth.ts, src/middleware/jwt.ts Do NOT touch: src/routes/billing.ts `, model: "sonnet" }) // Agent status: "hired" -> ready for task

Hire. Assign. Monitor. Fire.

Every Claude Code agent in chAIrman follows a structured lifecycle designed for predictability and accountability.

1

Hire

Create an agent with a specific role, job description, and model choice (Sonnet for speed, Opus for complexity). The agent receives your project's CLAUDE.md and any relevant skills from the library. It knows the codebase before it writes a single line.

2

Assign

Give the agent a task through the backlog system. Each task defines exactly which files the agent can touch, which are off-limits, and what success looks like. Budget checks run before the agent starts. Dependencies on other agents are respected automatically.

3

Monitor

Watch agents work in real-time through the dashboard. See tool calls, file edits, and terminal output as they happen. Track costs per agent, per task, and per project. Get desktop notifications when tasks finish or if budgets hit 80%.

4

Auto-Retry

When an agent fails, chAIrman automatically replaces it with a fresh agent that inherits the original's handoff notes. Up to 3 retries by default. The replacement knows what the previous agent attempted and where it went wrong.

5

Fire & Archive

Completed agents are archived to the alumni roster with their full history: tasks completed, files changed, cost, success rate. Their knowledge is preserved. You never lose institutional memory when an agent finishes its job.

6

Rehire Veterans

Need an agent that already knows your codebase? Rehire a veteran from the alumni archive. The new agent gets the veteran's job description plus experience notes, cutting ramp-up time to near zero.

Wire agents into dependency chains

Real features don't happen in isolation. The database schema must exist before the API routes, and the API must work before the frontend can integrate. chAIrman's pipeline system lets you express these dependencies so agents execute in the correct order, automatically.

Agents that don't depend on each other run in parallel. Agents that do depend on each other wait until their prerequisites finish. Cycle detection prevents deadlocks. The critical path is computed so you know exactly what's bottlenecking your pipeline.

  • Define dependencies with depends_on: ["agent-id-1", "agent-id-2"] when assigning tasks
  • Blocked agents show status "waiting" until all dependencies resolve to "done"
  • Pipeline visualization in the dashboard shows stages, critical path, and estimated completion
  • Webhook notifications fire on pipeline completion for CI/CD integration
// Agent pipeline: DB -> API -> Frontend // Step 1: Schema agent (runs immediately) assign_ticket({ agent: "db-engineer", task: "Create users + sessions tables" }) // Step 2: API agent (waits for DB) assign_ticket({ agent: "backend-lead", task: "Build auth REST endpoints", depends_on: ["db-engineer"] }) // Step 3: Frontend (waits for API) assign_ticket({ agent: "frontend-lead", task: "Build login/signup UI", depends_on: ["backend-lead"] }) // Step 4: QA (waits for Frontend) assign_ticket({ agent: "qa-engineer", task: "E2E tests for auth flow", depends_on: ["frontend-lead"] }) // DB runs now. API waits. Frontend waits. // QA runs last. All automatic.
54
MCP Tools
10
Agent Templates
857
Skills Injected
3
Auto-Retries

Why managed agents beat raw prompts

Pasting tasks into Claude Code works for one-off edits. It falls apart when you need multiple agents coordinating on a real feature.

File ownership prevents conflicts

Each agent's ticket defines which files it can touch and which are off-limits. Two agents never edit the same file simultaneously. No merge conflicts, no overwritten work, no wasted tokens re-reading code another agent just changed.

Context injection beats copy-paste

Every agent automatically receives your CLAUDE.md, handoff notes from predecessor agents, team messages, and up to 3 matching skills from an 857-file library. You don't manually paste context; the system injects exactly what each agent needs.

Budget enforcement stops runaway costs

Set a dollar budget per project. chAIrman checks the budget before every task assignment and refuses to spawn agents that would exceed it. Per-agent cost tracking shows you exactly which tasks are expensive and which models are cost-effective.

Structured handoffs preserve knowledge

When an agent finishes, it produces a structured handoff document listing completed tasks, files changed, blockers hit, and recommendations for the next agent. This institutional memory compounds across your entire project timeline.

Claude Code agents FAQ

What model do Claude Code agents use?
Agents can use any Claude model available through Claude Code. chAIrman supports model aliases like "sonnet" (Claude Sonnet, fast and cost-effective for most tasks) and "opus" (Claude Opus, more capable for complex architecture, refactoring, and security work). You can set a default model per agent and override it per task.
How many agents can run in parallel?
chAIrman's smart scheduler calculates optimal concurrency based on your machine's CPU cores and available memory. The default cap is 10 concurrent agents, configurable via the CHAIRMAN_MAX_AGENTS environment variable. Each agent is an independent Claude Code process, so the practical limit is your hardware and your Anthropic API rate limits.
What happens when an agent fails?
chAIrman detects eight categories of failures automatically: auth failures, rate limits, model not found, context exceeded, permission denied, file not found, network errors, and API overload. When a failure occurs, the system replaces the agent and re-assigns the same task with the previous agent's handoff notes. This happens up to 3 times by default (configurable per agent).
Do agents have access to my entire filesystem?
Agents operate within the project directory you specify and run with Claude Code's permission bypass mode for uninterrupted autonomous work. The ticket system lets you explicitly define files_to_touch and files_off_limits per task, giving you granular control over what each agent can modify.
Can agents communicate with each other?
Yes, through a shared message board. Agents can broadcast messages to all team members or send direct messages to specific agents using output tags. The CEO (you, via Claude Desktop) can also post messages that get injected into agent prompts. Messages are delivered at task-assignment time, not mid-task, which keeps agents focused on their current work.
How does chAIrman differ from running Claude Code directly?
Running Claude Code directly gives you a single agent with a single context window. chAIrman gives you an unlimited workforce: parallel agents with dependency management, auto-retry on failure, structured handoffs between agents, a skills library that auto-injects relevant knowledge, alumni archives for veteran rehiring, cost tracking with budget enforcement, a real-time dashboard, and git auto-commit on every completed task. It's the difference between hiring one contractor and managing an engineering department.

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