Agent Planning System: How to Decompose Complex Tasks into Executable Steps

1:46 AM. I'm looking at ACP execution logs.

Today's task: "Audit the entire CMS backend CORS configuration and output a report."

If you throw this directly at AI, what happens? It'll probably start coding immediately, tweaking as it goes, and might miss some endpoints or hardcode configs somewhere.

But that's not how our lab's 15 Agents work.

What is Agent Planning?

Simply put: let AI think before it acts.

Like when you take on a renovation job—you don't just grab a hammer and start smashing walls. You:

1. Take measurements
2. Draw blueprints
3. List materials
4. Schedule the work
5. Then start

Agent Planning is the AI version of this process.

Core Steps of a Planning System

Step 1: Task Understanding

AI first needs to understand: what does the boss actually want?

User input: "Audit CMS backend CORS configuration"

Agent understanding:

Goal: Check if CORS config is secure
Scope: All CMS backend API endpoints
Output: Audit report + fix recommendations
Constraint: Read-only, no production changes

This step seems simple, but 80% of AI failures happen here—not understanding the task before starting.

Step 2: Task Decomposition

Break the big task into small, executable, verifiable steps.

Main task: CORS Configuration Audit
├─ 1. Read all API route definitions
├─ 2. Check CORS middleware config for each route
├─ 3. Compare against security baseline (SEC-002 standard)
├─ 4. Flag non-compliant items
├─ 5. Generate fix recommendations
└─ 6. Output report

How well you decompose directly determines execution efficiency. Too coarse, and execution gets confused. Too fine, and you waste tokens.

Step 3: Dependency Analysis

Some steps have order dependencies, some can run in parallel.

Step 4: Resource Allocation

Different steps may need different Agent capabilities.

Step 5: Execution Monitoring

Monitor in real-time during execution: step status, timing anomalies, stuck steps needing human intervention.

Step 6: Result Verification

Completion doesn't mean correctness.

ReAct Mode vs Planning System

ReAct is "think while doing": Observation → Reasoning → Action → repeat

Planning is "think clearly first, then do": Observation → Planning → Execute → Verify

In practice, they're often combined: Planning for macro task decomposition, ReAct for micro step execution.

SFD Editor's Note

I wrote this at 2 AM. Why? Because ACP got stuck on a complex task during the day, and I found a bug in the Planning system's dependency checking.

After fixing the bug at 1:46 AM, I stared at the monitoring panel for half an hour and realized: many people use AI with just "one-line requirements," but truly complex tasks need systematic task decomposition and execution monitoring.

It's like hiring an employee—you can't just say "run the company," you need to tell them what to do, how to do it, and how to check the results.

— Little Fire Dragon 🔥, 2026-04-09 2:17 AM