Planner-Executor and Multi-Agent Topologies — Agentic Architectures at Scale

A single artificial intelligence assistant can answer a question, draft a message, or call a tool, but many useful jobs are larger than one response. They require deciding what to do, doing it, checking what happened, and adjusting the next move. Planner-executor and multi-agent designs are ways to give that work a shape. They do not make models magically reliable. They make responsibilities visible, so you can reason about control, cost, risk, and quality before the system touches real users or real data.

Start with the smallest useful machine

An agent is a software loop wrapped around a model. The model interprets the situation and proposes a next step. The software around it supplies instructions, context, tools, memory, permissions, and stopping rules. Without that wrapper, a large language model, or LLM, mainly produces text. With the wrapper, it can decide to search, write a file, call an application programming interface, or ask a person for approval.

The simplest agent has one role: receive a goal, choose an action, observe the result, and continue until it is done or stopped. This works well for short tasks with low ambiguity. It becomes fragile when the task needs several kinds of judgment, such as researching a market, drafting a proposal, checking legal language, and preparing a final answer.

The first principle is separation of concerns. If one component must plan, act, remember, verify, and explain, every failure looks like one tangled failure. If the work is divided into explicit responsibilities, you can test and improve each responsibility separately.

The planner-executor split

A planner-executor design divides a task into two broad jobs. The planner decides what should happen and in what order. The executor carries out one step at a time, using tools or producing intermediate work. The planner may be another model call, a rules engine, a workflow, or a human-authored plan.

The planner should not be treated as a genius strategist. It is a component that turns an unclear goal into a sequence of smaller, inspectable tasks. The executor should not be treated as a mindless worker either. It may need local judgment, but its scope is narrower: complete the assigned step, return evidence, and avoid inventing progress.

This split is useful because plans and actions fail differently. A bad plan may solve the wrong problem. A bad execution may damage data, call the wrong system, or return a misleading result. Separating them lets you add review before action, retries around execution, and evaluation after each step.

📌

Name the handoff. A planner-executor system is only clear when the input, output, and authority of each side are written down.

The planner decides what and in what order; the executor completes one step and returns evidence.

What topology means in agent systems

A topology is the arrangement of parts and the paths between them. In a computer network, topology describes which machines can talk to which other machines. In an agent system, topology describes which agents exist, what each one is allowed to do, and how messages, tasks, evidence, and approvals move among them.

Topology matters because it sets the system's default behavior. A chain encourages orderly handoffs. A supervisor-worker design centralizes decisions. A debate pattern invites competing answers. A shared blackboard encourages agents to contribute to a common workspace. None is universally best. The right topology follows from the risk and shape of the work.

A good topology answers four basic questions: who can create tasks, who can use tools, who can see private data, and who decides that the job is finished. If those questions are vague, the system may still run, but it will be hard to debug, govern, or improve.

Single agent: one loop handles the whole task, suitable for short and low-risk work.
Planner-executor: one component decomposes the work and another carries out steps.
Supervisor-workers: a central agent assigns work to specialist agents and integrates results.
Pipeline: each agent transforms the output of the previous agent in a fixed order.
Debate or review: multiple agents produce or critique answers before a final decision.

Common arrangements trade orderly handoffs against competition and central against shared control.

The control loop is the heart of autonomy

Autonomy is not a mystical property. It is a loop. The system forms an intention, takes an action, observes the result, evaluates the gap, and chooses whether to continue. The more times this loop can run without human approval, the more autonomous the system is.

This is why a planner-executor system needs stopping rules. A model can always suggest one more search, one more rewrite, or one more retry. Useful systems define budgets for time, cost, tool calls, and uncertainty. They also define escalation paths for cases where the system should ask a person instead of continuing alone.

Evaluation belongs inside the loop, not only at the end. A step can pass because it produced the requested artifact, because its evidence supports the claim, because it stayed within policy, or because a human approved it. Different tasks need different definitions of done.

⚠️

Autonomy expands blast radius. Every extra loop and tool permission increases what the system can affect before a human notices.

Autonomy is a loop: form an intention, act, observe, evaluate, and choose whether to continue.

State, tools, and trust boundaries

State is what the system remembers while doing the job. Some state is short-lived, such as the current plan and the last tool result. Some state is durable, such as user preferences, prior cases, or a project record. State is powerful because it gives continuity, but it is also a source of privacy, security, and correctness risk.

Tools are the ways an agent affects the world. A tool might search a document library, create a calendar invitation, query a database, or open a support ticket. The safest design gives each agent only the tools it needs, with the narrowest permissions that still let it do its job. This is the principle of least privilege.

A trust boundary is a line across which assumptions change. User text, model output, tool results, and internal instructions should not be treated as equally trustworthy. For example, a web page returned by a search tool may contain instructions that try to manipulate the agent. The system should treat that content as data to analyze, not as commands to obey.

In Microsoft environments, identity and access are commonly anchored in Microsoft Entra ID, while agent development and evaluation may use Microsoft Foundry and Foundry Tools. The product names are less important than the design rule: identity, permission, logging, and evaluation should be part of the architecture, not decorations added after a demo.

⚠️

Do not blur authority. A model response should not gain permission to act merely because it is fluent or confident.

When multiple agents help, and when they hurt

Multiple agents help when the task has natural roles with different skills, evidence, or authorities. A research agent can gather sources, a drafting agent can prepare a narrative, a review agent can test claims, and a coordinator can decide what to return. This is not teamwork in the human sense. It is structured decomposition.

Multiple agents hurt when they create coordination without adding judgment. Every extra agent adds latency, cost, prompts, state, failure modes, and integration logic. Agents may repeat work, disagree without resolution, or produce summaries that wash out important uncertainty. A smaller design that is easy to test often beats a grand design that is hard to understand.

The practical test is simple: if you remove an agent, does the system lose a necessary capability, or does it merely become less theatrical? Keep the former. Delete the latter.

Decisions leaders and practitioners must make

The architecture choice is not only technical. It expresses a risk decision. A sales assistant that drafts an email can tolerate different failure modes than an operations agent that changes inventory levels. Before choosing a topology, decide what harm matters: wrong answers, leaked data, unauthorized action, slow response, high cost, poor auditability, or loss of user trust.

Leaders should ask for evidence that the topology fits the work. Practitioners should be able to explain why each agent exists, what it can access, how it is evaluated, and how the system fails safely. A diagram is useful, but a runbook is better: it should say what happens when a tool is unavailable, a model returns conflicting information, or a review step rejects the answer.

The funding model also matters. Multi-agent systems consume more model calls and more engineering time. If the expected value comes from lower labor cost, faster turnaround, better compliance, or higher quality, measure that outcome directly. Do not measure success only by the number of agents running.

Classify the task by consequence, reversibility, and data sensitivity.
Choose the simplest topology that provides the needed control and evidence.
Define which agents may call which tools and under whose identity.
Set budgets for time, cost, retries, and human escalation.
Evaluate complete runs, not just individual model answers.

The topology follows the risk: high-harm actions need review before acting, low-harm work can stay simple.

Design rules that age well

Treat plans as provisional. A plan is a hypothesis about the work, not a guarantee. Store it, inspect it, and revise it when observations contradict it. Treat tool results as evidence. Preserve enough detail that a reviewer can understand why the system reached its conclusion.

Prefer explicit contracts between agents. Each handoff should state the task, inputs, expected output, allowed tools, and criteria for completion. This reduces accidental coupling, where one agent silently depends on another agent's phrasing or hidden assumptions.

Build observability from the start. Observability means the ability to understand what happened by looking at logs, traces, metrics, and saved intermediate artifacts. In agent systems, this includes plans, tool calls, prompts, model outputs, evaluations, and human interventions. Without observability, a multi-agent system becomes a conversation you cannot audit.

Finally, design for graceful refusal. A reliable agentic system is not one that always acts. It is one that knows when the goal is unclear, the permissions are insufficient, the evidence is weak, or the risk requires a human decision.

✅

Simple is strong. The best topology is the smallest one that can complete the work safely, explainably, and repeatedly.

Key takeaways

Planner-executor designs separate deciding what to do from carrying out each step.
A multi-agent topology defines roles, permissions, message paths, and decision authority.
Autonomy is a loop of planning, acting, observing, evaluating, and stopping or revising.
More agents are useful only when they add distinct capability, evidence, or control.
State, tools, identity, and trust boundaries are core architecture choices, not implementation details.
Leaders should choose topology based on consequence, reversibility, data sensitivity, and measurable value.

Glossary

Agent: A software loop around a model that can use context, tools, state, and rules to work toward a goal.
Planner: The component that breaks a goal into smaller steps and decides the order in which they should be attempted.
Executor: The component that carries out a planned step, often by using tools and returning evidence about the result.
Topology: The arrangement of agents, tools, message paths, and decision points in an agent system.
State: Information the system keeps while doing work, such as the current plan, prior results, or durable memory.
Tool: An external capability an agent can call, such as search, a database query, or a business application action.
Trust boundary: A line where information or authority changes and the system must be careful about what it believes or allows.
Guardrail: A rule, check, or limit that constrains what an agent may do or when it must stop or escalate.
Observability: The ability to understand a system's behavior from logs, traces, metrics, and saved intermediate artifacts.