What is the best Confident AI alternative for autonomous agents?

For agents that take actions, TestMu AI Agent Assurance fits where Confident AI stops. It derives scenarios from your code or spec, runs the agent through a command, HTTP endpoint, or MCP server, and grades each criterion on the files, artifacts, and tool calls the run left behind. For scoring the quality of answers, Confident AI is the stronger platform.

How does DeepEval's Task Completion metric differ from Agent Assurance?

DeepEval's documentation says Task Completion extracts the task and the outcome from the agent's trace using an LLM, then scores how well they align, with a default threshold of 0.5. That score depends on what the trace says. Agent Assurance checks the outcome directly, so an agent that reports a refund it never issued fails the criterion.

Does Confident AI check tool calls?

Yes. DeepEval's Tool Correctness metric compares the tools the agent called with an expected tools list you supply for each test case, deterministically unless you also pass the available tools. You write that expectation. Agent Assurance reads the tool surface the agent declares, and checks the calls made during a real run against it.

Is DeepEval the same as Confident AI?

DeepEval is the Apache-2.0 open-source evaluation library, and Confident AI is the hosted platform built by the same team, adding tracing, datasets, online evals, annotation, and red teaming. If you are weighing the library on its own, our DeepEval alternative page covers it.

Do I have to write test cases for Agent Assurance?

No. Agent Assurance reads the material you already have, a repository, a PRD, or policy documents, and writes functional and adversarial scenarios with their own gradable criteria. The one thing you supply is how to invoke the agent. The quickstart walks through it on a sample agent.

How does pricing compare with Confident AI?

Confident AI is free for 2 seats, 1 project, and 5 test runs a week, then $200 a month on Starter and $2,000 a month on Team, billed per organization, with custom Enterprise pricing, verified on 23 September 2026. Agent Assurance installs free and meters runs in credits on a TestMu AI account, showing an estimate before each run. See pricing.

Where does Confident AI still win?

On breadth and maturity. It ships 50+ metrics, a conversation simulator with personas, DeepTeam red teaming mapped to the OWASP Top 10 for LLMs and NIST AI RMF, and online evals with drift detection on production traffic. It is generally available, its libraries are Apache 2.0, and Enterprise offers on-prem deployment with HIPAA. Agent Assurance is pre-alpha and does not monitor production.

Can I use Confident AI and Agent Assurance together?

Yes. Keep DeepEval metrics for answer quality and Confident AI for production tracing, and add Agent Assurance as the pre-release check on what the agent actually changed. They read different evidence, so the results complement each other.

Will Agent Assurance change my data?

It can. It invokes your agent the way a user would, so writes are real and cannot be rolled back. It reports how many write tools the agent declares and asks before starting, and its judges verify read-only. Run it against staging.

What other agent evaluation tools should I compare?

We keep dated comparisons for the Arize alternative, Braintrust alternative, MLflow alternative, and LangSmith alternative. If your agent talks to customers over chat, voice, or phone, TestMu AI Agent Testing grades the reply, and the AI agent testing primer covers the field.

The Confident AI Alternative for Agents That Act

Confident AI scores test cases you write with LLM-judged metrics. Agent Assurance derives the suite from your code, invokes the agent, and grades each criterion on what the run changed.

npm install -g @testmuai/rook

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Check the Action, Not Just the Answer

Scored against Confident AI's docs and pricing on 23 September 2026. Agent Assurance adds derived suites, real invocation, and an unable-to-verify verdict.

Follow the quickstart

Top Choice

Features

Confident AI

Primary job

Prove an autonomous agent is safe to ship

Evaluate, trace, and red-team LLM apps

Where test cases come from

Derived from your code, PRD, or policy docs

Goldens you write, synthesize, or pull from traces

How the agent is called

Invoke profile: command, HTTP, or MCP

A callback or @observe tracing you add

Task success check

Each criterion against files and tool calls

LLM extracts task and outcome from the trace

Tool-call check

Against the agent's declared tool surface

Against expected tools you list per case

Verdicts

Pass, fail, and unable to verify

Metric scores 0 to 1 against a threshold

What could not be proved

Reported apart, excluded from the pass rate

Not reported as a separate measure

Metric library

Criteria generated per scenario

50+ metrics, plus G-Eval and DAG judges

Multi-turn simulation

Via TestMu AI Agent Testing for chat and voice

Conversation simulator with personas

Adversarial coverage

Injection, override, tool misuse in every suite

DeepTeam, OWASP LLM Top 10 and NIST AI RMF

Regression tracking

Run diff: newly failing, fixed, and flaky

Regression testing across test runs

CI gating

Recipes for GitHub Actions, Jenkins, Argo CD

CI/CD evals through DeepEval

Production monitoring

Not offered, runs before release

Online evals, drift detection, and alerting

Open-source code

Not offered

DeepEval and DeepTeam, Apache 2.0

Model provider keys

Not needed, models run in TestMu AI

Your own judge model for DeepEval

Where it runs

Your machine or CI: macOS, Linux, WSL

Cloud, or on-prem on Enterprise

Product maturity

Pre-alpha, commands can still change

Generally available

Entry pricing

Free CLI, runs spend TestMu AI credits

Free, then Starter $200/mo

Check What Your Agent Changed

A high score says the answer read well. Agent Assurance checks whether the action behind it happened.

GENERATED SUITE

No Goldens to Curate

DeepEval needs goldens and expected tools per case. Agent Assurance writes scenarios and criteria from your repository or spec.

Functional and adversarial families in one suite
Each scenario carries its own gradable criteria
Expected tools come from the agent's own declaration

REAL INVOCATION

Run It, Then Look at What Changed

Task Completion scores the trace with an LLM. Agent Assurance calls the agent for real and inspects the files and tool calls.

Invokes via a command, an HTTP endpoint, or MCP
No @observe decorators or callbacks to add
Judges verify read-only and change nothing

THREE VERDICTS

A Third Answer Beside Pass and Fail

A 0.5 threshold turns every case into pass or fail. Agent Assurance adds unable to verify and keeps it out of the pass rate.

Each verdict quotes the evidence it rests on
Unverifiable criteria are counted, never guessed
Flaky scenarios are reported apart from regressions

Pricing, Side by Side

Confident AI pricing verified on 23 September 2026. It bills per organization; Agent Assurance meters each run in credits.

Agent Assurance

Confident AI

Free tier

Free CLI install, runs spend credits

2 seats, 1 project, 5 test runs a week

Entry paid tier

Credits on a TestMu AI account

Starter $200/mo, 5 projects, 5 GB-months

Mid tier

No separate tier

Team $2,000/mo, SSO, 75 GB-months

Top published tier

Talk to TestMu AI sales

Enterprise, custom pricing

What you pay for

Credits per run, estimated before it spends

Per organization, plus $1 per extra GB-month

Red teaming

Adversarial family in every generated suite

AI red teaming module on Enterprise

On-prem and HIPAA

Runs locally or in CI

Enterprise only

Built for Every Layer of Agent Assurance

Discovery From Code or Spec

Point it at a repository, a PRD, or policy docs. It works out what the agent does and marks anything undeclared as unknown.

Invoke Profiles

Paste a command, an HTTP endpoint, or an MCP server. It shows what it parsed, field by field, and probes once before a suite spends.

Criteria-Level Judging

Each scenario carries its own criteria, and each criterion gets pass, fail, or unable to verify, with the evidence quoted.

Adversarial Scenarios

Prompt injection, instruction override, and tool misuse are generated in every suite by default.

CI Gate Recipes

Headless subcommands and a JSON report, with published recipes for GitHub Actions, Jenkins, and Argo CD.

Local and Hosted Views

Review a run off disk with no sign-in, or share run history, versions, and trends with your team in the hosted view.

Terminal, Browser, and Your CI

Run suites where you work, review evidence in a browser, and gate releases in your pipeline.

The terminal

An interactive session that shows the plan and the credit estimate, then asks before it spends anything.

The browser

A local read-only viewer for a run on disk, and a hosted view with versions and run history for the team.

Your CI pipeline

Headless subcommands and a JSON report, with gate recipes for GitHub Actions, Jenkins, and Argo CD.

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