Act–Observe Closure
Every action produces a structured observation that closes the loop back to the agent's reasoning — not a fire-and-forget invocation.
This page is the complete instruction page for one pattern called "Act–Observe Closure." It explains the problem this pattern solves, the idea behind the solution, when you should (and should not) use it, and what happens afterward — both the good effects and the costs.
This matters because building AI agents is not just about making them clever. It is also about making them safe and predictable. Following a well-tested pattern like this one helps avoid common mistakes, and shows you exactly which safety rules and regulations it connects to, listed under "Standards Mesh" on this page.
Context
Agents that act without reliable observation of the result drift into confabulation. They assume success, plan on top of an imagined state, and cascade errors.
Problem
Open-loop action destroys the feedback that makes iterative agents work.
Forces
- Action latency vs observation completeness
- Synchronous vs asynchronous tool semantics
Solution
Every tool call returns a typed result: success/failure, structured payload, side-effect summary, and a confidence or freshness marker. The agent's next reasoning step is fed the full observation, not a simplification.
Applicability
- All ReAct-style agents
- Any action with side effects
- Long-horizon tasks
Anti-Patterns
- Treating HTTP 200 as semantic success
- Discarding tool output metadata
Consequences
- +Reliable iteration
- +Errors caught at the next step
- −Slightly more token usage
- −Tool authors must define result schemas