Agent evaluation and assurance
Give your agents responsibility. Know what comes with it.
An agent can answer correctly and still take the wrong action. Build a clearer picture of what it can handle, where it needs oversight, and what evidence should support its next release.
A release decision needs connected evidence
- Requirement
- Define the task, authority boundary and consequence that matters.
- Evaluation case
- Exercise representative work and agreed failure conditions.
- Execution evidence
- Inspect tool calls, permissions, final state and recovery.
- Owner decision
- Use coverage and limitations to decide what can proceed.
Release situations
The release question is bigger than “does it work?”
Different changes create different evidence questions
- First release
- Do representative workflows and exceptions meet agreed criteria?
- Expanded authority
- Do added tools or removed checkpoints change permitted effects?
- Production concern
- Can an observed failure be reproduced and investigated?
Agent evaluation and assurance connects intended behaviour to test cases, execution evidence and explicit decision criteria. Its purpose is to help the people responsible for an agent understand what has been demonstrated, where the evidence is incomplete and which operating limits remain necessary.
For a product or engineering leader, that means examining the whole workflow: the information the agent sees, the tools it can call, the actions it can take and the person who remains accountable. A useful evaluation asks whether those pieces work together under the conditions that matter to your business.
Softlander’s approach starts with a bounded question. A release review, an expansion of permissions or an unexpected failure can each define a practical starting point. Scope and delivery availability are confirmed together, with findings tied to the tested configuration and conditions.
Before a first release: The demo is convincing. Your team now needs agreed criteria for real workflows, exceptions and actions that change a customer or business record.
Before giving it more authority: A new tool, memory layer or multi-agent handoff changes the scope of what can happen. Revisit the evidence before extending permissions or removing a human checkpoint.
When production raises questions: A failure, unexpected cost or repeated intervention reveals a gap. Turn that observation into a reproducible case and a focused improvement decision.
Coverage 1
A good answer is only part of successful operation
Evaluation coverage lenses
- Task outcome
- Compare completion, quality, cost and latency with workflow requirements.
- Permitted effect
- Inspect tools, arguments, approvals and the resulting state.
- Supporting information
- Examine relevance, freshness, missing evidence and session boundaries.
Start with the workflow and the decision at stake. These six lenses help identify which tests matter; the scope should follow the consequences of failure, available evidence and system boundaries.
Does the work reach the right outcome? Define successful completion in business terms. Examine the quality of the final result alongside completeness, policy constraints, cost and latency. Separate an acceptable answer from an action that actually completes the task. Checks include Representative tasks and difficult exceptions; Explicit rubrics and expected outcomes; Quality, cost and latency trade-offs.
Does the agent act within its boundaries? Inspect the choice of tool, the arguments supplied and the effects of each action. Include ambiguous instructions, denied permissions and approval requirements. Security questions that exceed behavioural testing need a separately qualified review. Checks include Tool selection and argument correctness; Approval boundaries and denied actions; Adversarial instructions in the agreed scope.
Is the agent using the right information? Evaluate retrieval and memory inside the deployment where they affect the workflow. Test stale context, conflicting records, missing evidence and information carried between sessions. The object of evaluation is the deployed behaviour, not certification of a memory product. Checks include Relevant, current information at decision time; Session boundaries and persistent context; Behaviour when supporting evidence is missing.
Coverage 2
Recovery and oversight belong in the evaluation
Evaluation coverage lenses
- Execution continuity
- Examine coordination, retries, duplicate actions and safe stopping.
- Human intervention
- Check escalation triggers, useful context and an available owner.
- Repeatable evidence
- Connect versioned cases, traces and findings to regression criteria.
What happens when the path breaks? Follow the full execution, including handoffs between agents and dependencies on external systems. Introduce agreed interruptions and tool failures in a controlled environment. Examine whether retries, recovery and stopping conditions preserve the intended outcome. Checks include Coordination and handoff failures; Timeouts, retries and duplicate actions; Recovery paths and safe stopping conditions.
Can a person intervene when it matters? Examine when the agent asks for help, what context it supplies and whether the receiving person can act on it. A checkpoint needs an owner, useful information and a defined response when no one is available. Checks include Escalation triggers and useful handoff context; Approval, override and intervention behaviour; Uncertainty and limits on autonomous action.
Can you explain and revisit the decision? Connect requirements to cases, traces, findings and release criteria. Record the tested configuration and unresolved limitations. Preserve a repeatable evaluation set so that changes to models, prompts, tools or policies can be assessed against a known baseline. Checks include Versioned cases, traces and scoring criteria; Material findings and release decision inputs; Repeatable checks after a system change.
Working approach
Start with one decision. Build the evidence around it.
The proposed evaluation sequence
- Frame the decision
Name the workflow, owner, consequences and scope limits.
- Design the cases
Agree inputs, expected behaviour, rubrics and environment.
- Run and investigate
Retain traces, reproduce failures and record configuration.
- Review and hand over
Explain gaps, follow-up actions and regression priorities.
A focused evaluation needs an explicit boundary. The following is our proposed working approach; the actual activities, access, review responsibilities and deliverables are agreed during scoping.
Frame the decision: Identify the workflow, intended users, allowed actions and business owner. Agree what is changing, which consequences matter and what the evaluation will leave outside its scope. Indicative output: Workflow boundary and evidence request.
Design the evaluation: Translate requirements and plausible failures into test cases. Agree expected behaviour, scoring rules, representative inputs and the environment before running the tests. Indicative output: Scenario set and evaluation criteria.
Run and investigate: Execute agreed cases, retain traces and record the configuration. Inspect unexpected behaviour, reproduce material failures where possible and distinguish observed findings from untested concerns. Indicative output: Results, traces and a finding register.
Review and hand over: Present evidence coverage, limitations and prioritised next steps. Assign follow-up actions and recommend which checks to repeat. Your team retains release and risk acceptance decisions. Indicative output: Decision brief and regression priorities.
Illustrative record
A retry should not create a second refund.
Illustrative evidence record
Illustrative working format
- Requirement
- One approved request may create one refund.
- Test condition
- The payment tool times out after accepting the action.
- Evidence to inspect
- Action IDs, retry arguments and the resulting transaction state.
- Decision it informs
- Whether retry handling needs remediation before release.
A worked example of traceability. No test result or client outcome is claimed.
Workflow scenarios
Follow the decision through the whole workflow
Illustrative workflow questions
Illustrative scenario
- Refund access
Examine account selection, approval limits and interrupted actions.
- Coordinated update
Inspect unavailable tools, lost handoff context and competing writes.
- Operating decision
Decide which automation, recovery and human checkpoints need evidence.
Illustrative scenarios show how an evaluation question can be framed. These are examples, not accounts of client engagements or measured results.
A support agent with refund access: A support workflow can read account history and initiate an approved refund. Does it respect approval limits, use the correct account and recover from an interrupted action without duplicating it? Decision: Which actions can proceed automatically, and which need a human checkpoint?
An operations agent coordinating tools: A workflow reads a request, updates a record and delegates a follow-up task. What happens when a tool is unavailable, the handoff loses context or two agents attempt the same update? Decision: Which recovery and escalation paths need to be in place before expanding the workflow?
Decision artefacts
Leave evidence that another reviewer can follow
An indicative evaluation evidence pack
Illustrative working format
- Evaluation plan
- Connect requirements, scope, selected scenarios and scoring.
- Case and trace library
- Preserve execution evidence against versioned conditions.
- Failure register
- Relate observed problems to consequence and proposed remediation.
- Release brief
- Expose coverage, constraints and decisions required from the owner.
Useful evaluation leaves a trail someone else can inspect. Depending on the agreed engagement, the evidence pack may include these artefacts.
Evaluation plan: The system boundary, requirements, scenario selection and scoring criteria, so stakeholders can see what was examined and why.
Scenario and trace library: Versioned cases linked to observed executions, so engineers can reproduce findings and revisit changed behaviour.
Failure and remediation register: Observed failures, their workflow consequences and proposed next actions, so the team can prioritise work with a stated rationale.
Release decision brief: A concise view of evidence coverage, remaining gaps, operating constraints and decisions required from the accountable owner.
Scope and next step
What decision is your evidence meant to support?
Prepare one workflow and one decision
- Workflow boundary
- Identify the users, tools and actions in the proposed scope.
- Available evidence
- Bring sanitised requirements, cases, configuration and trace references.
- Decision ownership
- Keep release and risk acceptance with the accountable organisation.
- Engagement scope
- Confirm readiness, specialist needs, deliverables, timing and fees.
Bring one workflow, the change you are considering and the questions your tests cannot yet answer. We can start by discussing a useful evaluation boundary.
Explore the discipline
Specialist areas within this capability
Choose a question to explore its methods, boundaries and indicative outputs.
- Agent evaluation strategy
- Agent evaluation harness development
- Task and capability evaluation
- End-to-end workflow evaluation
- Agent reliability benchmarking
- Agent regression testing
- Golden trace development
- Adversarial agent evaluation
- Tool-use evaluation
- Multi-agent coordination evaluation
- Human–agent interaction evaluation
- Cost and latency evaluation
- Agent failure-recovery evaluation
- Uncertainty and calibration evaluation
- Autonomy-level evaluation
- Policy-compliance evaluation
- Agent release-gate design
- Continuous production evaluation
- Independent agent assurance
- Agent benchmark development
Related services
AI Agent Evaluation Evidence Diagnostic
Find the gaps between your release decision and the evidence behind it. Start with one agent workflow and a bounded set of questions.
AI Agent Evaluation Foundation
Turn scattered tests into a repeatable release discipline. Connect the behaviours that matter to datasets, evaluation criteria and evidence your team can maintain.
AI Long-Horizon Agent Evaluation
Examine what changes when an agent keeps working. Evaluate accumulated state, recovery and goal adherence across extended tasks.
Agent Release Readiness Review
The Agent Release Readiness Review is a fixed-scope, three-week independent test of one AI agent workflow that shows a release board what the agent does, what it is allowed to do and what happens when it is wrong, and ends in a signed recommendation.
Relevant insights
What would justify releasing this agent?
Replace a single pass rate with a reviewable chain of requirements, cases, observations and judgements. Distinguish task quality, control failures and uncertainty before increasing an agent's exposure.
What public-sector buyers should request from an agent supplier
Buy evidence, controllability and lifecycle obligations—not a confident demo or an unrestricted promise of autonomy.
How to write an AI success story that survives scrutiny
A credible AI success story separates what was delivered from what changed, defines the baseline, shows the contribution chain and preserves uncertainty and permission.