Designing a Planner Agent: Decomposition and Sequencing

What a Planner Agent Actually Does

Here's the thing, when you build a multi-agent system, someone has to break the big messy request into smaller, executable pieces. That's the planner agent. It sits at the top of your workflow, looks at the incoming task, and decomposes it into a sequence of subtasks that executor agents can actually run. Think of it as the project manager who writes the ticket backlog while the engineers (executors) do the implementation.

From what I've seen, this separation, planning vs. executing, is what makes multi-agent systems composable and debuggable. The planner doesn't need to know how to query a database or call a payment API; it just needs to know what needs to happen and in what order. Andrew Ng has documented this as one of the core agentic design patterns: keeping reasoning separate from execution reduces complexity and keeps context windows clean.

For a customer support inquiry, the planner might identify that you need both billing data and a troubleshooting step. It generates the plan, "first, query billing; second, draft response; third, escalate if unresolved", then hands off each subtask to specialized executors. The planner monitors progress and can replan if something fails. That's the pattern.

Check your understanding

You're building a travel booking system. A user asks to "plan a weekend trip to Paris under $2000." Which of these is the planner agent's job?

Task Decomposition: Breaking Down the Big Request

The core skill of a planner is task decomposition, turning "Help this customer" into "Step 1: retrieve order history, Step 2: check refund eligibility, Step 3: draft response." You're identifying the logical subtasks, their dependencies, and the order they need to run in. Some tasks are sequential (you can't draft a response until you have the data), others can run in parallel (searching flights and hotels at the same time).

Here's what matters: each subtask should be scoped narrowly enough that an executor agent can handle it without needing the full context of the original request. Shunyu Yao's ReAct research showed that limiting scope reduces reasoning complexity, executors get planner-generated instructions, not the original user query. This keeps token counts down and prevents executors from wandering off-task.

In production, I've seen planners that generate dependency graphs, "Task B depends on output from Task A", and track which subtasks can be parallelized. When a diagnostic agent in an SRE incident system discovers the root cause is different than expected (database issue, not deployment), the planner can dynamically replan mid-workflow. That replanning loop is what makes this pattern resilient.

Scope Executors Narrowly

Check your understanding

A planner agent receives this request: "Generate a sales report for Q2, email it to the CFO, and post a summary in the #finance Slack channel." Decompose this into 3-4 subtasks with clear sequencing. What dependencies exist between steps?

Handoff Protocols and State Management

Once the planner has a task list, it needs to hand off work to executor agents and track what's happening. This is where most multi-agent systems fail in production. Research shows that 93% of failures manifest at the handoff, the edge between agents, through data gaps (you didn't pass all the context the executor needed), referential drift (the executor misunderstood what "the order" referred to), signal corruption (data got mangled in transit), and capability mismatches (you handed a task to the wrong agent).

Effective handoffs require explicit validation at the transfer point. In frameworks like LangGraph, you define typed state schemas (using TypedDict in Python) so every agent knows exactly what fields it's receiving and what it needs to emit. The planner writes to shared state, "current_step": "retrieve_order", "order_id": "12345", and the executor reads from it. When the executor finishes, it writes results back and signals completion. Harrison Chase and the LangChain team built this pattern into LangGraph with durable checkpointing, so workflows survive restarts without losing context.

Here's the gotcha: state can't just live in agent memory. Production systems need persistent state with checkpointing. Before LangGraph 1.0, state was ephemeral and workflows died on server restarts. Modern systems use PostgresSaver, AsyncSqliteSaver, or DeltaChannel for incremental updates. When an executor times out or a pod evicts, the planner can resume from the last checkpoint instead of starting over. That's the difference between a demo and a production system.

Handoffs Are Where Things Break

Check your understanding

Match each handoff failure mode to the scenario that exemplifies it:

Replanning and Error Handling

Plans don't always survive contact with reality. An API times out, a subtask returns unexpected results, or the executor discovers the initial assumptions were wrong. A good planner agent has replanning logic, it monitors execution, detects when something went sideways, and generates a revised plan on the fly.

For transient failures, rate limits, network blips, server 503s, you want exponential backoff retry logic. Retry with increasing delays (1s, 2s, 4s, 8s) until the dependency recovers. But don't retry everything. Authentication failures, bad requests, and context overflows won't fix themselves with another attempt. You're just burning tokens and time. When a dependency is actually down, circuit breakers should route the task to an alternative agent or escalate to a human.

In an SRE incident workflow, the planner might generate an initial diagnostic plan. The executor agents run checks, discover the root cause is different than expected, and report back. The planner pivots the plan, drops irrelevant subtasks, adds new ones, and continues. That iterative refinement loop is what makes multi-agent systems resilient. Microsoft Research's AutoGen team (now AG2) showed that agent debate and iterative replanning could produce results that single-pass prompting couldn't touch.

When to Retry, When to Replan

Key takeaways

• A planner agent decomposes complex tasks into sequenced subtasks with clear dependencies, then hands off execution to specialized agents.
• Scope executors narrowly, they should receive planner-generated instructions, not the full original request, to reduce reasoning complexity and token bloat.
• 93% of multi-agent failures happen at handoffs through data gaps, referential drift, signal corruption, and capability mismatches, validate every transfer point.
• Use typed state schemas and persistent checkpointing (PostgresSaver, AsyncSqliteSaver) so workflows survive restarts and timeouts.
• Build replanning loops into your planner so it can pivot when assumptions are violated or transient failures escalate into structural issues.

Your product check-in

Apply “Designing a Planner Agent: Decomposition and Sequencing” to a product or workflow you know. What would you try, what could go wrong, and what evidence would help you decide?