Plan-and-Execute Agents

Separating 'what to do' from 'doing it'

ReAct-style agents decide their next action one step at a time, reasoning fresh after every observation. That works well for short, exploratory tasks, but it has a cost on long tasks: the model re-derives context and strategy at every single step, which means it can drift, forget earlier intent, or thrash between approaches.

Plan-and-execute agents address this by splitting the agent into two distinct roles:

Planner — given the goal, produces a full (or near-full) sequence of steps upfront, before any action is taken.

Executor — carries out each step in the plan, one at a time, using tools as needed, and reports results back.

The planner commits to a strategy before execution starts. The executor's job is narrower and more mechanical: do this specific step, not figure out what to do next.

The basic architecture

A typical plan-and-execute loop works like this:

1. The planner LLM receives the user's goal and produces a numbered list of steps (often as structured output, e.g. a JSON array of step descriptions).
2. The executor takes the first step, and — usually running its own mini ReAct loop with the available tools — completes it, producing a result.
3. The result is appended to a running record of completed steps.
4. Before moving to the next step, a replanning check can run: does the plan still make sense given what happened? If a step failed or returned unexpected results, the planner can revise the remaining steps rather than blindly continuing.
5. This repeats until all steps are done, at which point a final synthesis step produces the answer.

Step 4 is what separates a good plan-and-execute implementation from a rigid one — without it, the agent is brittle against any step going differently than expected.

Why planning upfront helps on long tasks

On tasks with ten, twenty, or more steps, deciding the full scope of work before starting has real advantages:

Coherence — the executor doesn't need to "remember" the overall goal at every step; it just executes the step it's given, with the plan itself holding the structure.

Parallelism — independent steps in a plan can sometimes be executed concurrently (e.g., fetching reports for three competitors simultaneously), something a strictly sequential ReAct loop can't easily do since each action depends on the previous observation.

Auditability — a reviewer can read the plan before execution starts and catch a flawed approach early, rather than only discovering problems after the agent has burned through several tool calls.

Cost predictability — because the number of steps is roughly known in advance, you can estimate token and API cost before running the task.

The tradeoffs

Plan-and-execute isn't strictly better than ReAct — it trades one set of failure modes for another:

Early commitment risk. If the planner misunderstands the task or the environment turns out to be different than expected, the whole plan can be built on a wrong assumption. Without replanning, the executor will faithfully carry out a bad plan.

Extra latency and complexity. A separate planning call, plus the overhead of tracking plan state and deciding when to replan, adds engineering complexity that a single ReAct loop doesn't have.

Harder for truly open-ended exploration. Tasks where you genuinely don't know what the next step should be until you see the previous result (debugging an unfamiliar codebase, open-ended research) are a poor fit for rigid upfront planning.

When to reach for each pattern

What's next

Both ReAct and plan-and-execute assume a single agent doing all the work. For tasks that benefit from specialization — different agents handling research, coding, and review — multi-agent orchestration patterns extend these ideas across multiple cooperating agents, covered next.