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When autonomy is justified, begin with one agent and a deliberately small tool set or with an explicit workflow that places an agent only where interpretation is required. Add parallel agents, reviewers, custom branches, or human approvals only for a defined reason. The right choice depends on variability, precision, latency, cost, failure impact, tool count, review needs, and the organization’s ability to monitor the system.
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Decide whether the work needs an agent
An AI agent is useful when it must interpret a changing situation, decide what to do next, call tools, and continue through several steps toward an outcome. That is different from asking a model to generate text once or retrieve an answer from a known collection.
Agent infrastructure is not automatically an upgrade. It introduces more model calls, state, permissions, failure modes, and monitoring responsibilities. Microsoft’s architecture guidance advises using the simplest pattern that meets requirements and moving to more powerful patterns only when the scenario demands them.
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Use ordinary code or a conventional model for stable work
Use deterministic code when the rules, sequence, and outputs are known in advance. Examples include validating fields against a fixed schema, calculating a charge from a published formula, routing an order by a fixed region rule, or enforcing a hard approval threshold. A conventional model can handle bounded classification or extraction when the decision does not require the system to choose tools or alter its plan.
This approach is usually easier to test, explain, secure, and operate. An agent may add flexibility without adding business value if every case follows the same path.
Use RAG for static retrieval without action
Retrieval-augmented generation is a better fit when users need answers grounded in a fixed or slowly changing document set and the system does not need to take actions across other services. A policy assistant that searches approved manuals and cites the relevant passage is a retrieval problem if it does not decide which downstream system to update.
RAG becomes less sufficient when the next action depends on what was found, multiple systems must be queried in a variable order, or the input is incomplete and the system must resolve ambiguity.
Use an agent when autonomy changes the outcome
Agent reasoning is justified when the workload has one or more of these properties:
- The next action depends on information discovered during the task.
- The system must select among several tools or services rather than follow one fixed sequence.
- Inputs are incomplete, contradictory, or expressed in natural language.
- The task spans multiple steps whose path cannot be fully specified beforehand.
- Different cases require different evidence, questions, or actions.
Even then, keep the agent’s authority narrow. Flexible reasoning does not require unrestricted access or permission to complete every action.
Describe the workflow before selecting a model
Write down the work as a business process, not as a request for “an autonomous assistant.” Identify the intended outcome, the inputs, decisions, tools, outputs, exceptions, and the person accountable for the result. Mark each step as stable, variable, or dependent on interpretation.
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Record the outcome and decision owner
Define what “done” means in observable terms. For example, a claims process might require a classification, a documented reason, and a recommendation sent to a licensed reviewer. The agent’s answer is not the outcome if a human must still approve or if another system must be updated.
Assign a named business owner and document decision rights. Microsoft’s process guidance recommends redesigning a core business process before automating it, with a clear owner who can resolve disputes about policy, exceptions, and acceptable risk.
Map inputs, tools, and exceptions
- Inputs: What arrives, in what formats, and with which missing or unreliable fields?
- Decisions: Which choices are rules, and which require interpretation or evidence?
- Tools: Which databases, APIs, search systems, calculators, or transaction services are needed?
- Outputs: What must be returned, recorded, sent, or approved?
- Exceptions: What happens when evidence conflicts, a tool fails, or the request is outside policy?
- Ownership: Who is accountable for the final decision and who can change the process?
This map exposes whether an agent is solving a real source of variability or merely hiding an ordinary workflow behind a chat interface.
Match the workload to an orchestration pattern
The patterns below are alternatives, not a maturity ladder. Select the one that fits the dependency structure and risk of the task.
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| Pattern | Use it when | Main trade-off |
|---|---|---|
| Non-agentic code, conventional model, or RAG | Rules and steps are stable, or retrieval is static and requires no tool use or multistep reasoning | Less flexibility, but it avoids unnecessary agent complexity |
| Single agent with tools | A bounded task needs several steps, external information, and flexible tool selection | A practical starting point; performance can decline as task complexity and tool count grow |
| Explicit workflow with agent steps | The process and gates are known, but selected steps benefit from language-model reasoning | Clearer control, but the designer must encode sequence and decision points |
| Sequential multi-agent | Distinct stages have linear dependencies and each stage uses the prior stage’s output | Clear handoffs suit predictable pipelines; unnecessary when work is independent |
| Concurrent or parallel agents | Subtasks are independent, multiple perspectives are useful, or elapsed time matters | Requires aggregation and sufficient model capacity; unsuitable for strict sequence or cumulative context |
| Review, critique, or iterative refinement | Output needs a dedicated check or repeated improvement against a defined criterion | Can improve checking, but every extra pass adds latency and operating cost |
| Human-in-the-loop | A decision is consequential, subjective, sensitive, or requires final approval | Adds a review point and integration work while placing judgment at the critical step |
| Custom branching logic | Business-specific branches do not fit a standard pattern | Flexible, but increases development, debugging, and maintenance work |
Start with one agent when the task is bounded
A single agent with a small, well-described tool set is often the best first implementation for an open-ended but contained task. Give each tool a narrow purpose, explicit input and output contracts, and permission boundaries. Define when the agent must stop, ask for clarification, or escalate.
Do not assume that adding tools makes an agent more capable. As the tool list and task complexity grow, the agent may choose the wrong tool, take longer, or fail to complete the task. Split responsibilities only when the division is clear enough to improve reliability or ownership.
Use an explicit workflow when the process is known
An explicit workflow can control the overall sequence while an agent handles only reasoning-heavy steps such as extracting facts from a document, resolving a natural-language request, or drafting a recommendation. This hybrid design preserves gates, retries, and audit points in code or a workflow engine instead of asking one model to remember the entire process.
Choose this pattern when compliance, repeatability, or operational visibility matters more than fully open-ended planning.
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Sequential multi-agent designs fit a pipeline in which stage two cannot begin until stage one produces an output. A document-understanding stage might pass structured facts to an eligibility stage, which then passes a recommendation to a communications stage. Define the handoff schema and failure behavior for every boundary.
Do not use sequential agents merely because several roles sound impressive. Each handoff adds coordination, context transfer, model calls, and another place for an error to enter.
Use parallel agents only for genuinely independent work
Parallelism is appropriate when subtasks can run without one another’s intermediate results—for example, independent analyses of separate documents or multiple perspectives that an aggregator can reconcile. It can reduce elapsed time, but it needs an aggregation strategy, enough model capacity, and a rule for handling disagreement.
Parallel agents are a poor fit when steps require strict order, shared evolving context, or cumulative decisions. In those cases, concurrency can create contradictory actions or force a later component to reconstruct missing history.
Add review or critique for a defined quality risk
A reviewer agent or iterative refinement loop is useful when you can state what the check must detect: unsupported claims, missing fields, policy violations, or failure to satisfy a format. A vague “second opinion” is difficult to evaluate and may simply repeat the first model’s mistake.
Budget for the additional model calls. Review improves the chance of catching an error, but it also increases latency and operating expense and can introduce disagreements that require a human or deterministic rule to resolve.
Compare the decision factors before committing
Evaluate candidate architectures against the workload rather than choosing by reputation or novelty. The following factors should be recorded for each option.
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Task variability and precision
High variability favors flexible reasoning; high precision favors explicit rules, constrained outputs, validation, and approval gates. A task can need both: use an agent to interpret an unstructured request, then pass its result through deterministic checks before any action.
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Latency and operating cost
Count model calls, tool calls, retrieval steps, retries, and review passes. A parallel design may shorten elapsed time while increasing total calls. A sequential design may be simpler to reason about but slower. There is no universal cost or latency threshold in the available guidance; establish limits from the service level the business actually promises.
Failure impact and reversibility
Classify what happens if the system is wrong. A mistaken draft can be edited; an incorrect payment, account change, eligibility decision, or customer communication may cause financial, legal, or reputational harm. The higher the impact, the narrower the permissions and the stronger the approval, logging, rollback, and exception requirements should be.
Tool count and permission surface
Every tool expands both capability and risk. Minimize the set, separate read from write access, validate arguments, and scope credentials to the resources required for the task. A model should not receive a general-purpose administrative tool when a narrowly defined operation is sufficient.
Need for independent review
Ask whether the work benefits from a genuinely independent check and whether that check can be expressed as a criterion. If review is required because a person must exercise judgment, use a human approval step rather than disguising the requirement as another model call.
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Monitoring and staffing
Agent systems need owners who can inspect traces, investigate failures, update prompts or policies, and respond to drift. Include that operational work in the decision. An architecture that appears cheap to run but cannot be monitored safely is not a low-cost option.
Set decision rights, approvals, and boundaries
Write a permission policy before production. It should state what the agent may recommend, what it may execute, what requires approval, and how sensitive or ambiguous cases reach a person.
Separate recommendation from execution
For consequential actions, let the system gather evidence and propose an action while a named reviewer approves it. If automatic execution is justified, require structured validation, enforce policy checks outside the model, and make the action reversible where possible.
Define escalation triggers
- Required information is missing or contradictory.
- The request falls outside the approved policy or confidence range.
- A tool returns an error, stale data, or an unexpected schema.
- The action affects a person, money, customer access, safety, privacy, or compliance status.
- The system cannot explain which evidence supports the proposed action.
Escalation is part of the design, not an exception to be added after launch. Specify the receiving team, required context, response time, and whether the process pauses or rolls back.
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Production readiness should cover groundedness, access boundaries, privacy, safety, explainability, and risk review. Keep sensitive data out of prompts and logs unless its use is authorized. Record which sources, tools, policies, and model outputs contributed to a decision, subject to applicable privacy and retention rules.
Run a disciplined pilot
A pilot should test the architecture against representative cases, not showcase demonstrations. Select examples that include ordinary work, ambiguous inputs, missing data, tool failures, policy boundaries, and likely misuse.
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Define success criteria before testing
Measure the outcome the business cares about: correct routing, complete extraction, policy-compliant recommendations, successful task completion, reviewer effort, response time, or safe escalation. Track quality and operational measures together. A system that is accurate but too slow for the service level, or fast but unsafe, has not met the requirement.
Do not invent a universal pass percentage. Set thresholds with the process owner and risk stakeholders for the specific decision. The reviewed guidance does not establish a numerical cutoff that applies across businesses.
Compare a simple baseline
Test the proposed agent against the simplest credible alternative: a rule, conventional model, RAG flow, or explicit workflow. Record where autonomy improves the result and where it only adds calls, delay, or failure surface.
Test disagreement and recovery
For multi-agent designs, test conflicting outputs and failed handoffs. For tool-using agents, test timeouts, malformed responses, duplicate actions, revoked credentials, and partial completion. Verify that the system stops safely and that a person can reconstruct what happened.
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Instrument every meaningful step
Capture inputs and outputs appropriate to your privacy policy, tool selections, latency, retries, approval events, escalations, and final outcomes. Correlate a user request with all model and tool calls so an investigator can follow the path without guessing.
Monitor quality, service, safety, and drift
After release, monitor accuracy against reviewed cases, service-level performance, safety signals, tool-error rates, escalation volume, and changes in input distribution. Re-evaluate when policies, documents, tools, models, or user behavior change. A workflow that was safe for one workload may become unreliable as the workload expands.
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Version prompts, tool schemas, policies, model selections, and routing logic. Test changes on a fixed evaluation set plus recent representative cases. Keep a rollback path for model or workflow updates, and require the business owner to approve changes to decision rights.
Common selection mistakes and their fixes
Starting with “multi-agent” as the goal
Symptom: Several agents exchange messages but no one can explain why each exists.
Fix: Begin with the simplest workflow that meets the outcome. Split a role only when it has a distinct responsibility, tool boundary, quality criterion, or ownership need.
Giving one agent every tool
Symptom: The agent selects irrelevant tools, loops, or fails to finish.
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Fix: Reduce the tool set, make descriptions and schemas precise, separate read and write operations, and add explicit stop conditions.
Using parallelism for dependent work
Symptom: Outputs conflict because one task needed another task’s result.
Fix: Make the dependency sequential or encode the shared state and aggregation rule explicitly.
Adding a reviewer without a testable criterion
Symptom: The reviewer repeats the original answer or produces unresolvable disagreement.
Fix: Define exactly what the review checks and how failures are handled. Use a deterministic validator or human approval when that is more appropriate.
Automating a broken process
Symptom: The system moves confusion faster but does not improve the outcome.
Fix: Redesign the process, clarify decision rights, remove unnecessary handoffs, and name the owner before automating the core workflow.
Measuring only model quality
Symptom: A strong-looking demo fails in production because of delays, permissions, incomplete inputs, or review workload.
Fix: Measure business outcomes, task completion, safety, service levels, operating cost, and human effort together.
Illustrative architecture choices
Policy question answering
If employees ask questions over a controlled policy library and receive cited passages without any system update, use RAG with access controls and a clear “cannot find support” response. An agent is warranted only when the answer must trigger a variable sequence of checks or actions.
Customer-support case handling
A bounded single agent can classify a case, retrieve account information, draft a response, and suggest the next tool call. Keep account changes or compensation behind approval unless the business has explicitly authorized automatic execution. An explicit workflow can enforce identity checks, policy gates, and logging while leaving language interpretation to an agent.
Financial or eligibility decisions
Use an agent, if at all, to collect and organize evidence or explain a recommendation. Put policy calculations, required disclosures, access controls, and final approval in deterministic or human-controlled steps. The potential impact on people and money makes unrestricted autonomous execution inappropriate.
A practical selection checklist
- Is the outcome defined and owned by a named business person?
- Which steps are fixed rules, static retrieval, interpretation, or open-ended planning?
- Does autonomy materially improve the outcome over code, a conventional model, RAG, or a workflow?
- What is the smallest tool set and permission scope that can complete the task?
- Are dependencies sequential, independent, or conditional?
- What latency, operating-cost, quality, and service-level limits apply?
- Which actions require approval, and what evidence must a reviewer see?
- How will missing data, tool failure, ambiguity, disagreement, and partial completion be handled?
- What logs, evaluations, rollback controls, and owners will support production operation?
- What evidence from a representative pilot will justify expanding or simplifying the design?
Revisit the choice as the workload changes. A single agent may be the right starting point, while an explicit workflow, specialized agents, or a human gate becomes appropriate after the task, tools, or risk profile evolves.
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