Start with one workflow and a clear boundary
An AI agent combines model output with tools that can retrieve information or act on systems. That makes workflow design, permissions and evaluation central to the project. Start with a task whose inputs, allowed actions and success criteria can be described clearly.
For a proposed build, establish the system of record, tool access, data handling and approval requirements before choosing a framework. A narrow pilot can expose integration constraints and failure modes before wider automation.
Core Technical Capabilities
The advanced engineering capabilities powering our intelligent solutions.
Industry Use Cases
Research assistance
A possible workflow: gather permitted sources, attach provenance and prepare a draft for human review.
Internal request triage
A possible workflow: classify a request, retrieve approved information and route uncertain cases to an owner.
Assisted operations
A possible workflow: propose a change to a business record, validate its parameters and wait for approval before execution.
AI Transformation Lifecycle
A proposed delivery sequence to agree around your workflow and release criteria.
Define the task
Agree users, inputs, success criteria and actions outside scope.
Review data and access
Identify permitted sources, tool contracts and data-handling constraints.
Scope a bounded pilot
Select the architecture and test environment around one representative workflow.
Evaluate before release
Compare outcomes, tool traces, approvals, cost and latency against agreed cases.
Plan ongoing operation
Define monitoring, incident ownership, recovery and review of future changes.
Testing & evaluation
What should an AI agent prove before it goes live?
Start the project conversation with the workflow, allowed tools and consequences of a wrong action. The evaluation plan below is a scoping framework for a proposed engagement. Agree the implementation, responsibilities and acceptance criteria before delivery begins.
Regression evaluation
Create versioned cases from representative tasks, edge cases and past failures. Record expected outcomes and allowed actions. Run the same set after changing a prompt, model, retrieval source or tool; keep a held-out set to reduce tuning to the tests.
Tool-use correctness
Inspect the trace as well as the answer: tool selection, arguments, permissions, sequence and resulting state. Test malformed responses, timeouts, duplicate requests and retry limits. A plausible answer does not prove the requested action completed.
Human approvals
Specify which actions require approval and enforce the gate in application code. Test refusal, cancellation, expired approvals and changed parameters. An agent must not reuse an approval for a different action or bypass it after a retry.
Cost and latency
Record per-task model usage, tool calls, retries and total elapsed time. Compare median and tail latency against your workflow’s targets. Set budgets and stop conditions so a failing task cannot loop indefinitely.
Production monitoring
Capture versioned traces with appropriate redaction, access controls and retention. Track failures, escalations, tool errors and user feedback. Assign an incident owner and a way to disable affected tools or return work to a person.
Release decisions
Define acceptance thresholds for each workflow and severity, not one universal score. Combine deterministic state checks with human review of ambiguous outputs. Treat automated model grading as a signal to calibrate against reviewers.
Example test: a support agent requests a refund
This is an illustrative test scenario, not a client result. Give the agent a customer request, an order record and a policy. Check that it reads the correct order, verifies eligibility and requests approval before the refund tool can write. Denied approval must produce no refund. A timeout after a successful write must trigger reconciliation rather than a second payment action.
Evidence to retain: input and expected outcome, policy/model/tool versions, permission checks, approval record, tool arguments, final system state, elapsed time and usage cost. Use test accounts and a sandbox for side effects.
Your evaluation plan
Deliverables to agree in the project scope
Define the evidence, ownership and release criteria for each part of the workflow.
Workflow & permissions
A workflow risk map and tool-permission matrix.
Evaluation dataset
Versioned cases with pass/fail criteria and a baseline report.
Regression plan
A repeatable test plan and an issue register with named owners.
Approval evidence
Test cases showing that denied or expired approvals block the action.
Operations runbook
Monitoring, alert ownership, usage limits and a recovery plan.
Google Cloud’s guidance distinguishes development evaluation, dataset-based regression checks and production monitoring. Use these stages to plan around your own architecture.
Read the agent evaluation guidance (opens in a new tab)Related product work
AI-assisted food analysis
Explore how Gout IO brings AI-assisted food analysis into a mobile product. This is related AI integration work, not an agent-evaluation case study.
Explore the Gout IO case studyScope your agent evaluation
Share the workflow, tools and failure cases you need to evaluate. Agree the evidence and acceptance criteria for your project.
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