Agent Planning
The process by which an AI agent decomposes a high-level goal into a sequence of concrete steps before executing them. Planning is what separates agents from simple prompt-response systems: given 'Research this prospect and send a personalized email,' the agent plans steps (search LinkedIn, check CRM, review company news, identify pain points, draft email, review against brand guidelines, send). Planning quality determines agent reliability—poor planning leads to wasted actions and missed steps.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
A coding agent receives 'Fix this bug where user avatars don't load on the profile page.' It plans: (1) read the error logs, (2) identify the relevant component file, (3) trace the image loading logic, (4) identify the broken API endpoint, (5) write a fix, (6) write a test, (7) run tests, (8) submit a PR. Each step depends on the prior step's result.
Frequently asked questions
- Do all AI agents need planning?
- No. Simple, single-step agents (classify this ticket, summarize this document) don't need planning—they execute one action. Planning matters for multi-step agents where the sequence of actions affects the outcome. The more steps and the more conditional logic, the more important planning quality becomes. Most production agents with 3+ steps benefit from explicit planning.