What is Decagon AI? Agents, procedures, and human review

Decagon AI is a platform for building and operating conversational agents that answer customers and carry out connected business workflows. Its Agent Operating Procedures↗ (opens in a new tab), or AOPs, combine instructions written in business language with code-backed actions. The practical question is which work your team can describe, connect, test, and approve. fenbs can keep your own evaluation findings and next steps available when another person or AI continues the review.

What work does a Decagon agent handle?

Decagon describes enterprise agents across chat, voice, and email, with tools for creating and overseeing them. Its product site↗ (opens in a new tab) presents a sales-led deployment with a demo, rather than a documented consumer chat account. Treat those public descriptions as a starting point for a scoped conversation about your requirements.

Say a retailer wants help with return requests. The proposed job contains several separate operations: understand the question, find the relevant policy, check the order, determine what is permitted, and either take an authorized action or send the case to a person. Write those operations down before discussing a demonstration. A fluent answer alone would show only part of the job.

The broader boundaries belong in our guide to AI agents for customer service. For Decagon specifically, focus the evaluation on how the written procedure, connected systems, and human review fit together for one real workflow.

How do AOPs turn a policy into a workflow?

An AOP gives the team a way to describe agent behavior in ordinary business language. That does not remove the technical work underneath. Decagon explains that business-facing procedures work with engineering-owned code and validation, including sensitive operations such as identity checks and refunds, in its account of why it built AOPs↗ (opens in a new tab).

For the retailer, separate a policy statement from an action. “Returns are allowed when these conditions hold” describes a rule. “Issue the approved return through this system” requires a working connection and checks. A reviewer needs to inspect both, because a correct policy paragraph cannot prove that an action uses the right order or customer.

• Write the event that starts the procedure: a customer asks to return an item.
• Name the approved source: the policy version the team has chosen for the trial.
• List required information and checks before any action is allowed.
• Define the permitted action and the conditions that require a person instead.
• Specify what counts as a completed request and what evidence the reviewer needs.

Keep the reason for each boundary alongside your own review notes. fenbs can preserve those human decisions through a shared work record, so a later assistant knows why the team limited the trial. The deployed controls still need to be configured and tested in the agent and connected systems.

What must connect before an agent can take action?

Decagon describes connectors, APIs, and MCP in its Integrations product area↗ (opens in a new tab). An API is a software interface; MCP is a standard way for an AI app to use an outside tool. These capabilities support access to live information and actions, but the public page does not establish a universal connection screen, authentication method, or permission policy for every deployment.

Ask the deployment team to demonstrate the exact connection your workflow needs. For the return example, use approved test records and verify which information can be read, which change can be made, and which request is refused. Include the case where information is missing. An agent should not get credit for completing an action whose prerequisites were never checked.

Keep secrets out of your evaluation notes. Record the connection name, its intended purpose, the person responsible, and a reference to approved setup instructions. There is no need to copy credentials or customer records into the lasting summary. For a closer look at checking source material and generated steps, see AI agents for data analysis.

How should people review the agent before release?

Decagon names AOPs, Integrations, Testing & QA, and Duet as public product areas. Its Testing & QA page↗ (opens in a new tab) describes simulations and checkpoints for behavior, tools, data, and business logic. Simulations can be repeated as the agent changes; passing them is evidence about those checks, not a guarantee for every future interaction.

1. Choose a small set of approved sample requests. Include routine, ambiguous, and out-of-scope cases.
2. Write the expected answer and permitted actions before running each case.
3. Observe the result and inspect the action path, including any transfer to a person.
4. Record failures separately from unanswered implementation questions.
5. Have the responsible person decide whether to change the procedure, narrow the scope, or continue testing.

For example, a missing order should test the missing-information path. A request outside the return policy should test the boundary. Do not replace either with a second easy return simply because it produces a tidier demonstration. You want to know where the workflow stops as well as where it succeeds.

Which assumptions cause an evaluation to go wrong?

A common review mistake is treating a product concept as proof of a finished deployment. Write down what was actually shown, with its scope. “The sample return reached the reviewer with the expected information” is useful evidence. “The agent handles returns” leaves too many conditions unstated.

Another mistake is mixing a proposed requirement with a confirmed capability. Keep separate labels for documented, demonstrated, and still to verify. A question about access, contract scope, or administration belongs in the last group until someone supplies evidence. Silence in a public page is not proof that a feature is absent.

For a side-by-side purchasing evaluation, use Sierra vs Decagon. For the work of helping colleagues adopt a chosen process, see team AI adoption. The AI at work hub covers adjacent workflows. Those are different decisions from understanding the parts of a Decagon agent.

How to keep your Decagon review with fenbs

Keep what you learned so you can carry on from exactly where you left off, on any device and in any other AI you connect. Save the approved procedure summary, test findings, open questions, and next check. Leave customer conversations and restricted operational records in the systems your organization has approved.

1. Create a free account.
2. Write what you are checking and which actions must wait for a person.
3. Save each approved test summary: what you expected, what happened, and what still needs checking.
4. Connect Decagon to fenbs using Integrations. Ask the person setting up Decagon to check the connection and who may use it first.
5. Before stopping, write the next step so another person or connected AI can continue the review.

fenbs is useful for the review record. It does not build or run the customer-facing agent, enforce its refund rules, or replace Decagon simulations. If all you need is to operate the deployed agent, use the approved operating tools for that job.