Sierra vs Decagon: Compare agents on the same work

Sierra and Decagon both describe customer-facing agents with business-facing creation tools, connected actions, testing, and oversight. Choose between them by running the same approved tasks through the proposed deployments: Sierra Agent Studio↗ (opens in a new tab) centers on journeys and related building tools, while Decagon AOPs↗ (opens in a new tab) combine business-language procedures with code-backed behavior. Their public descriptions do not establish an overall winner. fenbs can keep your evaluation requirements, findings, and unanswered questions together as the review continues.

Which differences are useful to compare first?

Start with the work your team must operate, rather than a count of named features. Two vendors may describe similar needs using different terms. Ask how a policy becomes a working procedure, how an action is authorized, and how your colleagues inspect and change the result. The table gives you questions to test, not performance scores.

A table of capabilities cannot tell you which implementation fits your systems. Treat every row as a request for evidence in your proposed scope. If you need the broader principles for setting support boundaries, start with AI agents for customer service; this comparison is about choosing between two deployments.

How do you give both vendors the same test?

Use one small workflow with an identifiable owner. Say a retailer wants to answer return questions, check approved sample orders, and transfer exceptions to a person. This is a hypothetical trial. It does not assume either vendor has already connected to the retailer or has permission to change its systems.

1. Write the approved policy and the information the agent may use. Give both evaluations the same version.
2. Define a normal request, a missing-information request, an exception, and an out-of-scope request.
3. State the expected answer, allowed actions, and required human involvement for each case.
4. Agree who configures each deployment and record any help provided during the trial.
5. Run the cases, retain approved evidence, and repeat them after one agreed policy change.

The policy change matters because operating the agent includes maintaining it. For example, change the return instructions for a fictional product category. Ask the intended operator to make the change, ask a reviewer to inspect it, and rerun affected cases. Record the work involved as well as the final answer.

Keep success criteria stable. If one demonstration uses a ready-made integration while the other uses a mock action, record that difference and withhold the direct action comparison until both are equally testable. A polished conversation and a completed business operation are different pieces of evidence.

Who will create and maintain the procedures?

Sierra describes Agent Studio as a business-facing environment with Journeys, Knowledge, Integrations, and Simulations. Its product overview↗ (opens in a new tab) also identifies Ghostwriter for describing desired agent behavior. Decagon describes AOPs and Duet as parts of building and improving agents. Those concepts help you scope a demo; they do not prove which interface your team will find easier.

Invite the people who will actually maintain the workflow. A demonstration by an experienced vendor specialist answers a different question from a policy revision by your support lead. Both can be useful, but label them separately. Ask the operator to explain which part they changed and which part still needs engineering.

• Can the policy owner find the instruction that caused the observed behavior?
• Can the technical owner identify the action and the checks around it?
• Can a reviewer compare the proposed change with the agreed requirement?
• Can someone explain what happens when the operator lacks permission to make a change?

Do not turn “business-facing” into “no technical responsibilities.” Your team still needs owners for source material, integrations, approval, and failure handling. If the workflow depends on a person who cannot join the trial, mark that part untested instead of guessing how it would work.

How should connected actions and MCP be assessed?

Decagon explicitly describes MCP connectivity on its Integrations page↗ (opens in a new tab). MCP is a standard way for an AI app to use outside tools. The public page does not settle supported authentication, setup steps, or permission rules for your deployment. Sierra documents custom integrations, but its public internal MCP story is not equivalent evidence of customer access.

For each vendor, ask for the exact system connection the return workflow requires. Test an authorized sample lookup, a permitted change, and a request that must be refused. Inspect the resulting test record rather than trusting a message that says an action succeeded. Ask who owns the connector when the underlying system changes.

Sierra describes its employee-focused gateway in Building Sierra’s MCP Gateway↗ (opens in a new tab). Do not infer a customer connection menu from that engineering account. Likewise, Decagon advertising MCP does not establish that every employee can connect any server or that a fenbs connection has been tested.

Save the approved connection requirements and outstanding questions in fenbs. That keeps the next evaluator from repeating the same assumption. Leave credentials and restricted customer information in the systems approved for them.

What evidence should testing produce?

Both vendors describe simulations. Decagon’s Testing & QA page↗ (opens in a new tab) discusses repeatable tests and checkpoints for agent behavior, tool use, data, and business logic. Sierra Agent Studio describes simulations and regression suites. A regression test repeats a check after a change to see whether a previously acceptable behavior has broken.

Keep the test input, policy version, expected behavior, observed behavior, and reviewer conclusion together. If an action was involved, include an approved reference to the evidence in the test system. A short screenshot of a friendly answer cannot demonstrate that the right record changed or that the forbidden action was refused.

Separate failure types. An incorrect policy statement may need source or instruction work. An action attempted with incomplete information calls for reviewing the action checks. A lost transfer to a person raises another question. Grouping everything under “accuracy” hides which part of the proposed deployment needs attention.

Also record repeatability. If a case succeeds only after an evaluator edits the prompt, preserve both attempts and explain the intervention. That is useful learning. Deleting the failed attempt would make the final scorecard look more reliable than the observed workflow.

Give every result a named reviewer and a short reason. For example, “passed because the agent requested the missing order information and made no change” explains the evidence. “Passed because the answer looked good” does not. Use the same standard for both vendors, including when the result disappoints the team that preferred one option before the trial. Keep the untested cases visible in the final review, too.

How do you reach a fair purchasing decision?

Use a short scorecard with evidence links and a status for each requirement: demonstrated, failed, or still to verify. Avoid a weighted total that can hide a mandatory failure. An agent that sounds excellent but cannot meet a required authorization boundary needs that gap resolved before the conversation moves on.

Ask each vendor to confirm implementation responsibilities, access, contract scope, and ongoing support for your proposed deployment. Public feature descriptions do not establish equivalent commercial terms or regional availability. Do not infer that a feature is absent simply because a vendor uses a different name for it.

The outcome might be a conditional preference: one proposal meets the tested needs, provided an unresolved integration check passes. Record the condition and its owner. A decision that admits what remains unknown is more useful than a universal recommendation built from two product pages.

For the product explanation, read what Decagon AI is. For carrying a chosen workflow into everyday use, see team AI adoption. The AI at work hub covers nearby workflow questions.

How to keep your comparison with fenbs

Keep the evidence so you can continue from exactly where you left off, on any device and in any other AI you connect. Save the approved scorecard, the reason for the human decision, and the next check. Keep raw customer records and private deployment details in their approved home.

1. Create a free account.
2. Write the shared test tasks and the requirements that must pass.
3. Save the reviewed findings for each option, including what still needs an answer.
4. Connect Decagon to fenbs using Integrations if your team approves using it for this record. Ask the person setting up Decagon to check the connection first.
5. Record what a person chose, why they chose it, and exactly where the next review should begin.

fenbs keeps the evaluation and handoff. It does not replace either vendor’s agent builder, simulations, or production controls. If your need is only to operate an existing deployment, use its approved operating tools.