This describes a reference architecture, not a feature list for every product. Actual integrations and approval paths are defined and tested for each deployment.
1. Define the goal and the boundary
An autonomous agent does not mean unrestricted software. Begin with one outcome, explicit inputs and a stopping condition. Define which actions it may take on its own and which require review. A service-intake agent, for example, might classify a request and draft a record without being allowed to approve a repair or make a financial commitment.
- Name the process owner and intended outcome
- Define permitted actions and prohibited actions
- Set a time, step or cost limit and an escalation path
2. Supply relevant, authorized context
The agent needs current information about the task: a customer's request, service history, policy or equipment details. Retrieve only the records the caller is allowed to access. Source dates and references matter because a plausible answer based on an old policy is still wrong.
- Identify the source of truth for each record
- Minimize sensitive data and respect retention rules
- Treat incoming documents and messages as data, not instructions
3. Give the agent narrowly scoped tools
Tools turn a language model's suggestion into an operation. Each tool should have a defined input format, server-enforced permissions and a clear result. Separate reading from writing; granting access to an account lookup should not grant permission to delete an account.
- Validate inputs before reading or changing records
- Enforce access where the operation executes
- Record the caller, action and result without logging secrets
4. Coordinate reasoning with predictable rules
A typical agent loop gathers context, proposes a next step, calls an allowed tool and checks the result. Use ordinary workflow rules for predictable transitions and AI for ambiguous language or document interpretation. Do not let the model redefine approval rules or its own permissions.
5. Pause for approval where the risk warrants it
A draft estimate, outgoing message or payment-related action can require human approval. The reviewer needs to see the proposed change and supporting evidence. Approval should apply to that specific action—not become blanket permission for later, materially different actions.
6. Execute safely and verify the result
After approval, validate the action again and read the result back from the authoritative system. Use duplicate protection for retries so a delayed response does not create two tickets or invoices. If the system cannot confirm success, report the uncertainty rather than claiming the work is done.
7. Monitor quality and handle failures
Test representative cases before release, including missing data, conflicting instructions and unavailable tools. Track errors, escalation frequency and whether the business outcome was achieved. An agent should stop when evidence is insufficient, not fill gaps with invented facts.
- Compare results against a manual or rules-based baseline
- Provide a human handoff with context and an error reason
- Review changes to prompts, tools and source material before rollout
A practical service-intake example
A request arrives describing a vehicle problem. An agent retrieves the permitted customer context, identifies missing equipment details and prepares a service-intake draft. An advisor checks the draft before saving or sending anything. The system records the approved action and confirms the resulting record. This is a reference workflow, not a claim that every portfolio product includes these capabilities.
New to these terms? Read the AI glossary.