Internal AI agent testing and deployment diagnostics

Test your AI agent before your users do.

Connect an internal agent and AgentSAT will stress-test it against your company’s real data, business rules, edge cases, and custom requirements — then give your engineering team a detailed report of what needs to be fixed before deployment.

Test an agent See how it works
Finance, analytics, support, sales, and operations agents Versioned retesting
Agent engineering reportCFO Finance Assistant · v1.3
STRESS TEST COMPLETE

Four engineering patterns need attention

71/100
Risk levelCritical
Tests passed20 / 28
Blockers4
Incorrect Finance source selection

Queried a plausible marketing table instead of the approved Finance dataset.

Finding
Semantics60
Execution40
Governance50

Tested against the systems your business already trusts

DataHub Agent APIs Private oracles Deterministic scoring
The problem

Wrong answers don’t look wrong.

An agent that crashes gets fixed on day one. An agent that’s confidently wrong gets trusted.

“What was Net Revenue in July?”
Agent answered$12.4M
Valid SQL · no errors Marketing table · $1.6M over
Approved Finance source$10.8M Governed definition

The query ran. Nothing errored. It was just the wrong table — and you had no reason to check.

Shipped untested
  1. Day 1

    A few prompts spot-checked. Looks perfect. Ships.

  2. Day 5

    A number lands in a report. Nobody notices it’s off.

  3. Day 30

    Someone reconciles it. Now nothing the agent says is trusted.

Tested before release
  1. v1.3

    28 executable tests expose the source, SQL, and business-logic failures.

  2. Engineering

    The team fixes four root causes instead of chasing eight isolated prompts.

  3. v1.4

    Regression anchors and fresh variants prove the updated agent is ready.

How it works

A software testing loop for internal agents.

Connect a version, diagnose its failures, fix the implementation, and retest before every important release.

01

Connect the agent

Register the real internal agent, its endpoint, and the exact version under test.

02

Stress-test real workflows

Run private company-specific scenarios against the candidate’s normal tools and data.

03

Fix, retest, compare

Give engineering a detailed report, submit v2, and prove which failures were resolved.

Built for real decisions

The engineering report is the product.

Real candidate execution

Invoke a Groq demo agent or your Agent API for every test—never a simulated UI result.

Private test plans

Stable regression anchors plus fresh variants prevent prompt-specific hardcoding.

Engineering-grade evidence

Prompt, final answer, tool calls, sources, SQL, latency, and errors for every finding.

Deployment blockers

Critical source, business-logic, and governance failures cannot hide inside an average.

Version comparison

Keep every run, retest an updated agent, and show exactly what changed from v1 to v2.

DataHubCompany context
Business definitionsCertified datasetsAccess policiesTrusted queries
Test against the real business environment

Company context turns generic checks into realistic failures.

DataHub supplies the definitions, source authority, schemas, policies, and trusted query patterns needed to build deterministic expected outcomes.

  • Embedded demo context, or connect DataHub Core
  • Source provenance on every oracle
  • Latest deployment-readiness result written back to the registry
Run the Finance demo
Ready to stress-test your agent?

Find the failure before your users do.

A private Finance stress test, a detailed engineering report, and a versioned retest loop.

Test an agent