Plan-and-Execute Architecture: Separation of Strategy and Action in Agentic AI
Plan-and-Execute Architecture: Separation of Strategy and Action
The core idea
Instead of letting a single LLM call do everything, you split responsibilities:
- Planner: produces a plan
- Executor: executes step-by-step
- Monitor: detects drift and triggers replanning
Why separation improves reliability
Mixing planning and execution often creates chaos: the agent changes the plan mid-run. With plan-and-execute, you can lock the plan, log it, and hold the system accountable.
Definitions of done for each step
Every step should define:
- Inputs
- Expected output (observable)
- Failure conditions
- Retry policy
Replanning triggers
Replan only when:
- A tool fails repeatedly
- New information invalidates assumptions
- The cost budget is exceeded
- User changes the goal
Production logging
Always log:
- Plan version
- Step outputs
- Tool calls
- Replan reasons
This is how you debug “the agent went crazy” incidents.
