ArchiChat

The agent lifecycle, end to end.

An agent's knowledge is only correct on the day it is trained. ArchiChat takes each specialized agent through four stages, Train, Deploy, Update and Evaluate, so that what it knows about its profession stays accurate for as long as it is in use. Your team assigns the work. The agent executes with context that is current.

  1. 01, once per agent

    Train

    The agent learns its professional domain and the internal context only your team can provide.

    • Regulation, standards, research and accepted practice in the domain
    • Your policies, conventions and architecture
    • Scope: what the agent covers and what it defers on
    • A definition of what "current" means for this domain
  2. 02, once per agent

    Deploy

    The agent ships with defined boundaries, interface contracts and quality gates.

    • Defined responsibilities and excluded areas
    • Interface contracts and data boundaries
    • Automated checks before any output reaches your team
    • Optional human-in-the-loop approval for high-risk actions
  3. 03, continuous

    Update

    Changes in the domain are monitored, filtered and folded into the agent's knowledge automatically.

    • Regulatory amendments and jurisdiction changes
    • Standard revisions and new certification requirements
    • Advisories and recommended mitigations
    • Shifts in accepted practice and new guidance
  4. 04, continuous

    Evaluate

    The agent is measured against the domain's definition of current, and against real task outcomes.

    • Scenario drills against acceptance criteria
    • Regression checks after every knowledge update
    • Feedback from real team usage
    • Quality criteria your team defines and can audit

Context and currency are different problems.

Only your team can provide the internal context an agent needs. ArchiChat provides the domain knowledge and keeps it current. These are separate responsibilities, and both must be owned.

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Further reading

Questions

What are the four stages of the ArchiChat AI agent lifecycle?
The AI agent lifecycle is a four-stage operating model for professional agents: Train, Deploy, Update, Evaluate. Train and Deploy happen once per agent; Update and Evaluate run continuously for as long as the agent is deployed.
How does ArchiChat keep agents from going stale?
The Update stage monitors each agent's domain for amendments, revisions, advisories and new guidance, filters out noise, and refreshes the agent's knowledge when a relevant change lands. Nobody re-prompts or re-trains manually.
What guardrails does ArchiChat apply when deploying an agent?
The Deploy stage establishes defined scope, interface contracts and quality gates. Outputs pass automated checks before reaching your team, and optional human-in-the-loop approvals are available for high-risk actions.
How does ArchiChat evaluate agent quality over time?
The Evaluate stage defines what current means for each agent's domain and measures the agent against it. Agents run scenario drills and regression checks after every knowledge update, so drift is caught before people notice it.
What does ArchiChat train each agent on?
Each agent is trained on the regulation, standards, research and accepted practice of its professional domain, plus the internal context your team provides: policies, architecture, conventions and scope.
How often should an AI agent's knowledge be updated?
As often as the domain changes, not on a fixed calendar. Scheduled re-training misses time-sensitive changes such as an amendment or advisory that lands between cycles. ArchiChat updates an agent when a relevant change is detected in its domain and evaluates it after each update, so the cadence is set by the domain rather than by a schedule.
What is the difference between RAG and keeping an agent's knowledge current?
Retrieval-augmented generation fetches documents at query time; it does not change what the agent knows or how it reasons about the domain. Keeping knowledge current updates the agent's understanding itself, in structured form, when the domain changes. ArchiChat treats retrieval as a complement to knowledge currency, not a substitute for it.
What is the difference between context and currency?
Context is the internal knowledge only your organization can supply: policies, conventions, architecture and scope. Currency is whether the external domain knowledge an agent relies on is up to date. They are separate responsibilities. Your team owns context; ArchiChat owns currency and keeps the domain knowledge current.
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