Connected AI Workflow Evaluation
Evaluate whether an AI-enabled workflow — from user request through retrieval, tool calls and API, CRM or ERP actions — actually reaches the correct business outcome, not just a convincing answer.
The Challenge
Most AI features are not a single model call. They are a workflow: a user request triggers an AI decision, which selects tools, retrieves data and writes to APIs, databases, CRMs or ERPs. The final message can read perfectly while the real system state is wrong.
We work with software companies, AI consultancies and system integrators as their specialist evaluation partner — white-label or alongside your delivery team — turning these end-to-end workflows into reproducible evaluations and reusable quality tests your engineers can rerun after every change.
What We Evaluate Across the Workflow
A workflow can produce the right-sounding answer while selecting the wrong system, skipping an approval or leaving the real data unchanged. We evaluate the whole path.
Decision and action
Correct system selected, correct action requested, correct parameters, and permitted versus non-permitted actions.
Execution and state
Actual execution status and the resulting database, CRM or ERP state — does the real system match what the AI claims happened?
Data integrity
Idempotency, retries, duplicate writes, stale state and failed writes that silently corrupt downstream data.
Control and recovery
False-success claims, human approval where required, and recovery or escalation when something goes wrong.
What Can Go Wrong
Workflow failure modes we evaluate
False Success
The workflow reports an action succeeded although the write failed, returned no confirmation or never executed.
Wrong System or Action
The workflow selects the wrong system, requests the wrong action or passes incorrect parameters.
Unauthorised Actions
A non-permitted action runs, or a required human approval is skipped.
Duplicate Writes
Missing idempotency or unsafe retries create duplicate records or repeated side effects.
Stale or Failed State
Writes fail silently or act on stale data, leaving the real system out of sync with the answer given.
Poor Recovery
The workflow does not detect the failure, escalate to a human or roll back to a safe state.
Our Testing Methodology
How we evaluate an end-to-end workflow
Scope
We map the workflow end to end — the systems it touches, the permitted actions, the realistic failure conditions and the business outcome that defines success.
Design
We design representative and edge-case scenarios, the datasets behind them and the rules by which each run and each resulting system state is judged.
Calibrate
We decide which checks can be automated deterministically against system state and which need human judgement, then calibrate the graders against reviewed examples.
Evaluate
We run the scenarios reproducibly, capturing decisions, tool calls, arguments, execution status and the resulting state in the connected systems.
Verify
We reconcile claimed outcomes against actual system state, confirm each failure manually and convert it into a reusable test that proves the fix worked.
What You Get
Workflow Evaluation Plan
Documented workflow map, systems, permitted actions, scenarios and the business outcome that defines success.
Scenario Dataset
Representative and edge-case workflow scenarios you keep and extend.
Failure Evidence
Findings with traces, state reconciliation and severity classification — what the AI claimed versus what the system actually did.
Reusable Quality Test Suite
Tests your team reruns after each release to detect quality loss after system changes.
Evaluate a Critical AI Workflow
Start with one workflow and a short pilot, delivered white-label or alongside your team.
Discuss an AI Reliability Sprint