Stop babysitting your AI. chAIrman deploys self-running Claude Code agents that plan their own work, recover from failures, learn from veterans, and deliver completed features -- without a human in the loop.
A copilot suggests the next line. An autonomous agent takes a ticket and delivers a working feature. Here is what separates the two.
| Capability | AI Copilot | chAIrman Agent |
|---|---|---|
| Scope of work | Autocomplete, single-file edits | Full tasks: multi-file features, tests, refactors |
| Failure handling | Shows error, waits for you | Auto-retry up to 3x with handoff to replacement agent |
| Context across sessions | Forgets everything on reload | Handoff docs + alumni archive persist indefinitely |
| Skills and knowledge | General training data only | 857 skill files auto-injected by task relevance |
| Coordination | Single user, single file | Pipeline orchestration with dependency chains |
| Supervision required | Constant -- every suggestion needs approval | None -- agents run with bypassed permissions |
| Learning from mistakes | No memory between sessions | Feedback loop writes corrections into skill files |
Autonomous AI agents are not a single feature. They are a stack of interlocking systems, each solving a different failure mode.
When an agent crashes or exits with a non-zero code, chAIrman automatically replaces it with a fresh agent that inherits the handoff document. The replacement gets the same task, the same context, and the predecessor's progress notes. Up to 3 retries before escalating. Eight stderr patterns are auto-detected: auth failures, rate limits, context exceeded, network errors, and more.
Every time a task is assigned, chAIrman scores 857 skill files against the task description using TF-IDF weighting, bigram matching, category tags, and synonym expansion. The top 3 matching skills are injected into the agent's prompt automatically. Agents get domain-specific knowledge without you having to find or attach it. Usage is tracked so popular skills surface faster.
Every data event from an agent resets two inactivity timers. At 10 minutes of silence, chAIrman emits an idle warning. At 30 minutes, it flags the agent as a zombie and suggests termination. The system also checks process liveness -- if a PID marked as "working" is actually dead, the agent is immediately flagged as errored and eligible for auto-retry.
Every 60 seconds, a running agent's handoff document and file changes are saved to disk. If the process crashes mid-task, the next agent (or a rehired veteran) picks up from a structured JSON snapshot that includes completed tasks, blockers, files modified, and recommendations. No work is lost. No context starts from zero.
When an agent finishes and is fired, its entire experience -- role, model, task history, files changed, cost, and success rate -- is archived in the alumni system. Future projects can rehire veterans with rehire_veteran, giving the new agent the old one's job description plus an experience summary. Proven agents ramp up faster and avoid known mistakes.
When you correct an agent's output, capture_feedback saves your correction as a markdown skill file. Every future agent working on similar tasks inherits that correction automatically through skills matching. The system gets smarter with every interaction. Your taste and quality standards compound across the entire workforce.
Real software projects have dependencies. The API must exist before the frontend can call it. The database schema must be defined before the ORM layer is built. Tests cannot run until the code they test is written.
chAIrman handles this with pipeline orchestration. When you assign a task, you can specify depends_on with an array of agent IDs. The task enters a waiting state and automatically launches the moment all dependencies finish. Cycle detection prevents deadlocks. Critical path calculation tells you which agent chain determines your total completion time.
This means you can define an entire sprint -- backend agent, frontend agent, testing agent, deployment agent -- in a single conversation. Each agent waits for its prerequisites, runs autonomously, commits to git, and hands off to the next stage. You check in when the pipeline completes, not at every step.
An autonomous AI agent in chAIrman is a Claude Code process running in your project directory with full filesystem access. It reads your code, understands the structure, writes new files, edits existing ones, runs tests, and commits to git. Here is a typical lifecycle:
You describe a feature in plain English. chAIrman's CEO workflow breaks it into a backlog with milestones and tickets. Each ticket maps to one agent with explicit file ownership -- no two agents edit the same file simultaneously. The agent receives its ticket along with your CLAUDE.md project context, matching skills from the library, messages from the team board, and handoff notes from any predecessor.
The agent then works independently. It reads the codebase to understand conventions. It writes code, creates tests, and iterates until the success criteria from its ticket are met. If it crashes, auto-retry handles it. If it finishes, the close handler runs an 11-step process: cleanup, parse cost and tokens, finalize the task, commit to git, update the briefing, resolve downstream dependencies, and archive the handoff. The next agent in the pipeline launches automatically.
You do not need to approve every file write, review every diff mid-task, or restart crashed processes. The system handles all of that. You review the finished output.
files_to_touch list (what this agent can edit) and a files_off_limits list (what it must not change). The CEO workflow requires that no two agents edit the same file simultaneously. Pipeline dependencies ensure sequential work happens in order. This eliminates merge conflicts and destructive overwrites.rehire_veteran creates a new agent pre-loaded with the veteran's job description and experience notes. The agent already knows your codebase conventions, common pitfalls, and what worked before. Combined with the feedback loop (corrections saved as skill files), the system gets measurably better with each project.Join developers who ship faster with chAIrman. From $19.99/mo.