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Start with one narrowly scoped agent, then add specialists only when a task needs separate expertise, tools, or authority. Give every role a written mission, input and output contract, tool list, permission boundary, escalation rule, and validation check. Choose a manager, handoff, or directed-graph design based on who should control the work; make state, retries, termination, and cancellation explicit; then trace and evaluate real runs before expanding autonomy.
What role-based AI agents are and why teams use them
A role-based agent is an AI worker with a defined responsibility rather than a general-purpose chatbot. Its role specifies what it is allowed to decide, which information it may read, which tools it may call, what format it must return, and when it must ask another agent or a person for help. A team workflow combines several such workers under an orchestration pattern.
Typical roles include intake or triage, research, execution, review, and publishing. The names are less important than the boundaries. A researcher may gather evidence but have no write access to production systems. An executor may update a ticket or draft a file but cannot approve its own change. A reviewer may reject or request revisions without changing the underlying record.
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Write the workflow contract before creating agents
Document the first workflow on one page. Keep the scope narrow enough that a person can inspect every output during the pilot. This contract becomes the reference for prompts, tool permissions, tests, and incident reviews.
Define the business outcome
State the result in observable terms, such as “produce a reviewed draft ticket response” or “classify an incoming request and route it to the correct queue.” Avoid goals such as “be helpful” that cannot be tested. Identify the final approver: a named team, role, or individual who owns the decision when the workflow reaches a boundary.
Specify the trigger and required inputs
Record what starts a run, whether it is a message, scheduled event, API call, or human button. List required fields, optional fields, accepted file types, maximum sizes, and the system of record. Decide what happens when a field is missing: ask the requester, route to intake, or stop with a clear error.
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- List the data the workflow may read, including workspace, project, tenant, and retention limits.
- List data it must never expose in prompts, logs, or outputs.
- Define the failure path: retry, return for clarification, escalate to a person, or terminate without side effects.
- Describe the approval required before an external action, publication, deletion, financial change, or permission change.
Choose a measurable acceptance test
Write a small set of representative cases before deployment. Each case should have an expected role output, required fields, prohibited actions, and an escalation condition. These cases become a regression set when prompts, models, tools, or routing change.
Design a role specification that downstream steps can trust
Every role should have six parts: mission, input contract, output contract, tools, authority limits, and escalation and validation rules. Put these in version-controlled configuration or another location with an owner and review history.
Mission
Describe one responsibility and its stopping point. “Extract the customer’s requested change and identify missing details” is a stronger mission than “handle the request.” Include the intended audience for the output and whether the role is advisory or action-taking.
Input and output contracts
Define required fields and types. A downstream role should not have to infer whether a paragraph is a final answer, a hypothesis, or an unverified note. A useful output contract can require:
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- evidence: source identifiers or tool results used;
- actions: proposed or completed actions, clearly distinguished;
- risks: unresolved uncertainty, policy concerns, or missing approvals;
- next_step: the exact role or person that should receive control.
Validate this structure before passing it onward. Reject malformed output, preserve the original response for diagnosis, and send a bounded correction request rather than allowing an unvalidated paragraph to trigger an action.
Tools and authority
Give a role the smallest useful tool set. A read-only search tool, for example, is safer than a broad connector that can also edit, delete, or send messages. Document each tool’s purpose, permitted parameters, rate limits, and expected error values. Authority should be explicit: “may draft,” “may create a ticket,” and “may publish” are different permissions.
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Escalation and validation
Specify conditions that require a person or another role: conflicting records, low-confidence extraction, sensitive data, policy exceptions, a failed retry budget, or a request outside the role’s mission. Add deterministic checks where possible, such as required fields, allowed status values, duplicate detection, and approval presence. A reviewer role should check the executor’s result against the original contract instead of merely asking whether the text sounds good.
Start with one agent, then split by stable responsibility
Implement one specialist with a clear instruction block and the minimum tools needed for the first workflow. Observe where it fails before creating a team. Splitting too early creates more prompts, state transitions, permissions, and failure modes without proving that collaboration improves the result.
Signals that one agent is enough
- The task has one data domain and one approval owner.
- The same tool set and policy boundary apply from start to finish.
- Intermediate reasoning does not need independent review.
- The output can be validated with a single contract and a small regression set.
Signals that a specialist boundary is justified
- A step needs distinct expertise or a different source of truth.
- A role must have read-only access while another role can write.
- An independent reviewer is required for safety, compliance, or quality.
- A stage can be reused by several workflows with the same contract.
- Different teams own the tools, approvals, or escalation paths.
Useful initial roles
| Role | Primary responsibility | Typical authority |
|---|---|---|
| Intake or triage | Normalize the request, classify it, and identify missing information | Read intake channels; create or update a routing record |
| Researcher | Gather and organize evidence from approved sources | Read-only access to specified repositories and search tools |
| Executor | Perform a bounded change after prerequisites are met | Write access only to named systems and operations |
| Reviewer | Check the result against policy and the output contract | Approve, reject, or request revision; no unapproved production edits |
| Publisher | Release an approved result to its destination | Publish-only access after an explicit approval signal |
Choose the coordination pattern deliberately
The coordination primitive determines who owns the user-facing result, how context moves, and where failures are handled. OpenAI’s agent documentation describes agents used as tools and handoffs; AutoGen documents directed graph execution for teams. These are architectural choices, not benchmark rankings.
| Pattern | Control owner | Best fit | Design cautions |
|---|---|---|---|
| Manager with agents as tools | A central manager | Stable decomposition where one agent owns the final answer | Keep specialist outputs bounded and validate them before the manager acts |
| Handoffs | The current specialist | Natural transfer of responsibility between roles | Define the context passed, the return path, and who can escalate |
| Directed graph or team controller | The graph or team controller | Explicit stages, branches, state transitions, and auditability | Requires more scaffolding, state design, and termination handling |
Manager pattern
Use a manager when a single agent should interpret the request, call specialists, reconcile their results, and present one response. Expose each specialist as a narrowly described capability. The manager should receive structured results, not an uncontrolled transcript, and should be unable to bypass required approval checks.
Handoff pattern
Use a handoff when the next specialist should own the conversation or task. Pass only the context that role needs: the normalized request, relevant evidence, current status, and constraints. Include a return or escalation route so a specialist cannot leave a run ownerless. Handoffs should be explicit events in logs, with the source role, destination role, reason, and payload version.
Directed graph or team pattern
Use a graph when sequence and branching must be visible and auditable. Nodes represent roles or deterministic checks; edges represent permitted transitions. This makes it easier to enforce “review before publish,” route exceptions, and resume from a known state. The trade-off is more implementation work and a larger surface for state, cancellation, and retry bugs.
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Design state, context, and resumability
Do not rely on an agent’s hidden conversation as the system of record. Store a run identifier, workflow version, current stage, normalized input, approved context, outputs, tool results, approvals, and error information in durable state appropriate to the sensitivity of the data.
Keep context bounded
Pass the minimum information needed for the next decision. Summarize long histories into facts with provenance, and retain links to the underlying records where policy permits. Separate user-provided text, tool output, and agent-generated conclusions so a later role can tell which claims were observed and which were inferred.
Version contracts and state
Store the role and workflow versions with every run. If an output schema changes, provide a migration or reject older state safely. A resumed run should not silently apply a new authority rule to an old approval. Make state transitions atomic where an external action and a status update must remain consistent.
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Make actions idempotent
Retries can duplicate writes unless each operation has an idempotency key or a precondition check. Before creating a record, search for an existing run-specific key. Before publishing, verify that the reviewer approval is current and that the target has not changed. Record tool calls and their results so operators can distinguish a timeout from a completed action whose response was lost.
Set termination, cancellation, and retry controls
Every run needs a finite control policy. Team presets can use automatic termination conditions, and external termination is necessary when an operator, policy system, or user must stop work immediately.
Maximum turns and timeouts
Set a maximum number of model turns for each role and for the complete workflow. Add separate timeouts for model calls, tool calls, and the overall run. A timeout should produce a known status such as blocked, preserve state, and identify whether it is safe to resume.
Retry policy
Retry transient transport or service failures with a small, bounded budget. Do not blindly retry validation failures, authorization errors, or a tool operation that may already have succeeded. Use increasing delays where appropriate, then escalate with the failing tool, attempt count, and last known state.
Termination conditions
- Terminate successfully when the final output contract passes validation and required approval is present.
- Terminate as blocked when required input, a tool, or an approval is unavailable.
- Escalate when policy, confidence, or authority rules require a person.
- Terminate on loop detection, turn exhaustion, deadline expiry, or a forbidden tool request.
External cancellation
Provide an operator control that marks a run for cancellation and propagates that signal to active roles and tools. A cancelled run must not start a new side effect. If a connector cannot cancel an in-flight operation, mark the uncertain result, prevent duplicate retries, and require reconciliation before resuming.
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Permission design is part of the agent architecture, not an afterthought. Workspace Agents use role-based access controls, and connected tools can allow users to act through connections created by the agent owner. Review that ownership model carefully: the agent’s identity, the initiating user, and the connector owner may have different rights and audit requirements.
Map groups to roles
Map workspace groups to the agents and tools they may invoke. Separate designers, operators, reviewers, and administrators. A person who can run a workflow should not automatically be able to edit its instructions, secrets, or approval policy.
Review every connector
- Use read-only scopes unless a write operation is required.
- Restrict records, projects, channels, and tenants to the workflow’s need.
- Store secrets in the runtime’s approved secret mechanism, never in prompts or source-controlled instructions.
- Log the actor, agent identity, connector identity, operation, target, and result.
- Define ownership and offboarding procedures for connections created by an agent owner.
Administrative setup for collaboration platforms
Slack and Teams integrations require administrative tenant connection and installation for the appropriate users or groups. Plan this step with the platform administrator, document which workspace or tenant is connected, and verify that test users cannot see channels or data outside their assigned scope.
Separate approval from execution
For consequential actions, require an approval artifact that names the proposed change, target, approver, time, and policy version. The executor should verify that artifact immediately before acting. Do not treat a natural-language statement in an old conversation as a durable approval.
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Choose where the workflow runs
| Runtime choice | Strengths | Trade-offs | Use when |
|---|---|---|---|
| In-application SDK | Full control over deployment, tools, state, approvals, and user experience | Your team owns reliability, security, scaling, and upgrades | The workflow is core to your product or needs custom controls |
| Managed agent harness | Less runtime infrastructure to operate and faster initial setup | Fewer deployment and execution controls; provider-specific limits may apply | The team wants managed operations and accepts the platform boundary |
| Sandboxed workspace | Isolation for files, commands, packages, and temporary workspace state | Requires careful resource, network, package, and data controls | An agent must manipulate files or run commands without touching production directly |
Choose based on authority and operational ownership, not only convenience. A sandbox does not make unsafe instructions safe by itself; restrict network access, filesystem paths, credentials, package installation, and command execution. Conversely, an in-application SDK still needs explicit state, approval, cancellation, and observability controls.
Trace runs and evaluate before adding autonomy
Tracing records the evidence needed to debug a multi-agent run: prompts and instruction versions, model calls, tool arguments and results, handoffs, latency, retries, failures, approvals, and final outputs. OpenAI recommends tracing for debugging followed by evaluation loops. Capture enough data to reconstruct a decision while applying retention and redaction rules to sensitive content.
Evaluate each contract independently
Score the intake role for classification and required fields, the researcher for evidence quality and provenance, the executor for correct tool use and side-effect boundaries, the reviewer for policy detection, and the publisher for approval enforcement. A final answer can look acceptable while one intermediate role silently violated its contract.
Build a regression set
Include ordinary cases, missing inputs, conflicting records, malformed tool responses, permission denials, timeouts, duplicate requests, cancellation during a tool call, and adversarial instructions embedded in retrieved content. Run the set whenever you change prompts, models, tools, permissions, or graph transitions. Measure your own workflow; the published guidance provides no universal accuracy, latency, adoption, or cost figures.
Use staged autonomy
- Shadow mode: the workflow proposes outputs while people continue the existing process.
- Assisted mode: approved users trigger bounded actions after reviewing the proposal.
- Limited automation: low-risk cases run automatically; exceptions always escalate.
- Expanded scope: add tools or roles only after traces and regression results show the existing boundaries hold.
Example: a controlled request-to-publication workflow
Consider a team that receives a content-change request and wants a reviewed publication draft. The following design is illustrative; adapt the contracts and permissions to your systems.
Stage 1: intake
The intake agent extracts the requested change, target location, deadline, requester, and missing details. It returns needs_input when any required field is absent. It can read the intake channel and create a routing record but cannot edit published content.
Stage 2: research
The researcher receives the normalized request and reads approved internal sources. Its output lists evidence identifiers, facts, conflicts, and unresolved questions. It has no publishing or write permission.
Stage 3: execution
The executor drafts the change in a staging location. It checks the request identifier and workflow version, uses an idempotency key, and returns the exact target and diff. It cannot publish.
Stage 4: review
The reviewer compares the draft with the intake contract and research evidence, checks required policy fields, and either approves, requests a revision, or escalates. Approval is a structured record, not merely a positive sentence.
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The publisher verifies the current approval, target identity, and unchanged draft hash before release. It records the publication result and stops. A mismatch routes back to review instead of silently overwriting the target.
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Rollout checklist
- Write the outcome, trigger, inputs, data boundary, approver, and failure path.
- Implement one specialist with minimal tools and a versioned instruction block.
- Define a machine-checkable output contract and reject invalid results.
- Identify stable responsibility or permission boundaries before splitting roles.
- Choose manager, handoff, or graph coordination and document the control owner.
- Persist run state, workflow version, approvals, tool results, and error status.
- Set turn limits, tool and run timeouts, retry budgets, loop detection, and external cancellation.
- Map workspace groups and connector scopes; test read, write, and approval permissions separately.
- Connect Slack or Teams only through the required administrative tenant and user/group installation process.
- Trace representative runs, including failures and cancellations, with appropriate redaction.
- Run a regression set for every role before granting more autonomy.
- Expand one boundary at a time and retain a human escalation path.
Troubleshoot common failure modes
The manager keeps calling the same specialist
Add a maximum-turn rule, detect repeated calls with unchanged inputs, and require the specialist to return a distinct blocked or needs_input status. Inspect the trace to determine whether the manager is missing a required field or ignoring a validation error.
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A handoff loses important context
Define a handoff envelope with the run ID, current contract version, normalized request, evidence references, approvals, and unresolved risks. Pass references to large artifacts rather than truncating them unpredictably. Add a test where the destination role must identify a deliberate conflict.
A retry creates duplicate records
Use an idempotency key and a precondition lookup before every side effect. If the connector timed out after accepting the request, reconcile the target before retrying. Mark the outcome uncertain when reconciliation is impossible.
The agent requests a tool it should not have
Remove the tool from its available list and enforce authorization outside the prompt. Log the request as a policy event, then inspect whether retrieved content or a user instruction attempted to override the role boundary.
Runs cannot be stopped reliably
Implement cancellation as durable run state checked before each new model or tool call. Propagate it to workers, disable new side effects after the signal, and document which external operations require manual reconciliation.
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Outputs sound fluent but fail requirements
Move quality checks into the output contract and reviewer role. Require evidence fields, allowed status values, and deterministic policy checks. Add the failing example to the regression set instead of relying on a broader instruction alone.
How to decide whether to add another agent
Add a role when it creates a durable boundary: distinct expertise, independent review, different data access, a reusable stage, or a separate owner. Do not add one merely to make the architecture appear sophisticated. If a single agent can complete the task with a small tool set, a clear contract, bounded context, and reliable evaluation, keep it that way.
When you do split, preserve a single source of truth for workflow state, make every transition observable, and ensure each specialist can stop safely. The quality of a role-based system comes less from the number of agents than from precise contracts, least-privilege tools, explicit control flow, and disciplined evaluation.
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