ArchiChat logo ArchiChat Agent Lifecycle

AI Agent Lifecycle

The AI agent lifecycle,
end to end.

Enterprise dev teams can't afford agents that require manual re-training every few weeks. ArchiChat trains specialized developer agents on your stack and cycles them through four continuous stages — Train, Deploy, Update, and Evaluate — to keep them current. Your team assigns work; the agent executes with context that is always accurate.

Train

Your stack, your standards

ArchiChat trains each agent on your team's stack, architecture, and org standards.

  • Architectural patterns specific to your system
  • Language and framework version targeting
  • Org coding standards and security baselines
  • Performance characteristics of your runtime
Deploy

Defined scope, clear guardrails

ArchiChat deploys each agent with defined boundaries, API contracts, and mandatory quality gates.

  • Agent scope: defined responsibilities and excluded domains
  • API contracts, event schemas, data boundaries
  • Automated tests and quality-gate checks before output
  • Optional human-in-the-loop approvals for high-risk actions
Update

Automatic knowledge refresh

ArchiChat pushes technology changes into agent knowledge automatically — no manual re-training required.

  • Library releases and version deprecations
  • Security advisories and recommended mitigations
  • Performance tuning techniques for updated runtimes
  • Ecosystem shifts and emerging patterns in your stack
Evaluate

Closed-loop quality improvement

ArchiChat measures each agent against acceptance criteria and real-task outcomes, then improves the model.

  • Scenario drills against acceptance criteria
  • Regression checks after each knowledge update
  • Feedback loops from real team usage
  • KPI-driven quality improvements per sprint cycle

Your team assigns work. The agent executes with up-to-date context — no re-prompting, no knowledge gaps.

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Frequently Asked Questions

What are the four stages of the ArchiChat AI agent lifecycle?
ArchiChat's AI agent lifecycle covers four continuous stages: Train, Deploy, Update, and Evaluate. Each stage keeps specialized developer agents accurate and production-ready without manual intervention.
How does ArchiChat keep developer agents from going stale?
ArchiChat's Update stage monitors technology changes and pushes them into agent knowledge automatically. When releases, deprecations, or security advisories emerge, the agent's training refreshes. Your team never re-prompts or re-trains manually.
What guardrails does ArchiChat apply when deploying an agent?
ArchiChat's Deploy stage establishes defined boundaries, API contracts, and quality gates. Outputs must pass automated checks before reaching your team. Optional human-in-the-loop approvals are available for high-risk actions.
How does ArchiChat evaluate agent quality over time?
ArchiChat's Evaluate stage measures each agent against acceptance criteria and real-task outcomes. Agents run scenario drills and regression checks after every knowledge update. Results feed a continuous improvement loop tied to team-defined KPIs.
What does ArchiChat train each developer agent on?
ArchiChat trains each agent on your architecture, language specifics, org standards, and security baselines. Training targets your codebase context, not generic patterns from broad datasets.
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