Planning and Reasoning in AI Agents

Planning and reasoning in AI agents is the process of decomposing a goal into an ordered set of smaller steps, identifying the dependencies between them, and executing them while monitoring each result. When a step fails or produces unexpected information, the agent revises the remaining plan instead of abandoning the goal or repeating the same unsuccessful action.

• Task decomposition: Dividing a broad objective into concrete steps that a single tool or action can complete.
• Dependencies: Relationships that specify which steps must finish before another step can begin.
• Plan-and-execute: An architecture in which a planner produces the complete plan first and an executor performs each step.
• Replanning: Modifying the remaining steps after a failure, a new observation or a changed constraint.
• Reasoning: The model's analysis of the goal, the available tools and the evidence, often expressed as chain of thought prompting.

For example, a coding agent assigned to fix a flaky integration test plans to collect CI history, rerun the test, compare logs, patch the code and open a pull request.

Key Characteristics of Planning and Reasoning in AI Agents

• Goal-oriented: Every step exists to advance a clearly defined objective with a verifiable completion condition.
• Structured: Plans are often represented as a list or a dependency graph, which application code can inspect and validate.
• Separation of roles: A planner model designs the steps, while an executor performs them through tool calling.
• Adaptive: Replanning replaces failed steps with alternatives that still satisfy the original dependencies.
• Stateful: Completed steps and intermediate results are preserved, frequently through memory in AI agents.
• Reviewable: A written plan can be approved by an engineer before any risky operation is executed.

How Planning and Reasoning in AI Agents Works

1. Interpret the goal: The planner analyses the objective, the constraints and the available tools.
2. Decompose the task: The planner generates discrete steps, each with a description and a list of dependencies.
3. Validate the plan: Application code confirms that every tool exists and that the dependencies contain no cycles.
4. Execute ready steps: The executor runs each step whose dependencies are complete and records the outcome.
5. Detect failures: A failed step, an unexpected result or an exceeded budget triggers a replanning decision.
6. Replan: The planner substitutes an alternative step or reorders the remaining work, then execution resumes.
7. Verify completion: The agent confirms the goal is satisfied, for example by checking that the test passes repeatedly.

Example: Plan-and-Execute with Replanning in Python

The program below executes a dependency-ordered plan for a flaky integration test and replaces a step that fails.

# Plan for "fix flaky integration test": step -> (description, dependencies)
plan = {
    "collect": ("Collect the last 20 CI runs of test_checkout_flow", []),
    "rerun": ("Rerun the test 50 times in isolation", ["collect"]),
    "compare": ("Compare logs of passing and failing runs", ["collect"]),
    "patch": ("Add an explicit wait for the payment stub", ["rerun", "compare"]),
    "open_pr": ("Open a pull request with the fix", ["patch"]),
}

# Sample step results, hard-coded for illustration (a real agent runs tools)
OUTCOMES = {"rerun": "fail: CI runner quota exceeded"}

def replan(plan, failed):
    """Replace the failed step with an alternative that reaches the same goal."""
    desc, deps = plan.pop(failed)
    plan["rerun_local"] = ("Rerun the test 50 times in a local container", deps)
    for name, (d, step_deps) in plan.items():
        plan[name] = (d, ["rerun_local" if x == failed else x for x in step_deps])
    return f"replanned: {failed} -> rerun_local"

done = []
while len(done) < len(plan):
    ready = [s for s, (_, deps) in plan.items() if s not in done and all(d in done for d in deps)]
    step = ready[0]
    result = OUTCOMES.get(step, "ok")
    print(f"{step:12} {plan[step][0]:50} {result}")
    if result.startswith("fail"):
        print("  ->", replan(plan, step))
        continue
    done.append(step)
print("Completed order:", " > ".join(done))

collect      Collect the last 20 CI runs of test_checkout_flow  ok
rerun        Rerun the test 50 times in isolation               fail: CI runner quota exceeded
  -> replanned: rerun -> rerun_local
compare      Compare logs of passing and failing runs           ok
rerun_local  Rerun the test 50 times in a local container       ok
patch        Add an explicit wait for the payment stub          ok
open_pr      Open a pull request with the fix                   ok
Completed order: collect > compare > rerun_local > patch > open_pr

• Dependency ordering: The patch step waits until both the rerun and the comparison are complete.
• Targeted replanning: Only the failed step is replaced, and every dependency on it is redirected to the new step.
• Deterministic stand-in: The replan function is a fixed rule here, whereas a production agent would ask the planner model for an alternative.

Applications of Planning and Reasoning in AI Agents

• Bug fixing: Reproducing a defect, locating the cause, writing a patch and opening a pull request.
• Dependency upgrades: Updating a library, running tests across services and correcting breaking changes.
• Incident follow-up: Converting a postmortem into ordered remediation tickets with owners.
• Infrastructure changes: Sequencing configuration updates, validation checks and staged deployments.
• Team coordination: Assigning plan steps to specialised workers in multi-agent systems.

Advantages

• Efficiency: Planning ahead avoids redundant actions that a purely step-by-step ReAct agent might take.
• Parallelism: Independent steps, such as rerunning tests and comparing logs, can execute concurrently.
• Oversight: A visible plan allows human-in-the-loop approval before sensitive operations.
• Resilience: Agent replanning recovers from failures without restarting the entire task.

Limitations

• Incorrect plans: The planner can omit necessary steps or invent tools that do not exist.
• Stale assumptions: A plan created early may rely on information that later observations contradict.
• Additional cost: Planning and replanning require extra model invocations and tokens.
• Replanning loops: Repeated failures can trigger endless revisions without a limit on replanning attempts.
• Evaluation complexity: Judging plan quality requires comparing steps, order and outcomes, not only the final answer.