Component-level agent evaluation
Find the mechanism behind the workflow result.
A completed task can hide fragile memory, missing context or accidental recovery. Examine the components that determine what the agent knows, how it plans and what its tools actually do.
Find the mechanism behind the workflow result
- Memory lifecycle
- Connects saved facts, correction, recall and deletion.
- Context assembly
- Selects the evidence available for a specific decision.
- Orchestration state
- Coordinates routing, delegation, retries and termination.
- Tool execution
- Turns chosen actions and arguments into external effects.
Discipline model
A successful workflow can hide a fragile component
The relationships that define the discipline
- Memory lifecycle
- Retained information can alter what later retrieval and planning consume.
- Context assembly
- Selection determines which facts and instructions reach generation.
- Orchestration state
- Routing and retries connect decisions across workers and external dependencies.
- Tool execution
- Actual effects establish whether the intended work completed once and correctly.
End-to-end results answer whether a task completed under the tested conditions. Component evaluation asks which mechanism produced that result and whether it behaves correctly when conditions change. A grounded answer can depend on a stale memory; a recovered workflow can contain a duplicate write. Isolating those mechanisms helps make a proposed fix testable.
Working approach
Use probes that separate competing explanations
Proposed investigation sequence
- Define memory lifecycle
State the decision and conditions the work must address.
- Map context assembly
Connect the relevant actors, configuration and evidence.
- Examine orchestration state
Use agreed evidence and controlled cases where appropriate.
- Review tool execution
Record limits, ownership and the condition for a next decision.
Map the actual component path before designing probes. Choose cases that separate memory accuracy from retrieval selection, context selection from generation, and planner decisions from connector effects. Capture intermediate state and repeat relevant conditions. Preserve a workflow-level check so a component improvement is not assumed to improve the overall system.
Working artefact
Connect the mechanism to the final workflow effect
Illustrative working record
Illustrative working format
- Memory lifecycle
- Define the subject and the requirement the record addresses.
- Evidence reference
- Link source, version and observed condition without copying sensitive material.
- Orchestration state
- Explain the finding or choice and its consequence.
- Tool execution
- Assign the next action and state what remains unresolved.
A component probe record identifies the mechanism, controlled condition, expected behaviour, intermediate evidence and final workflow effect. Record versions and provenance so a change can be compared. Observed failures, suspected causes and proposed remediation should remain distinct until the evidence supports the link.
Illustrative situation
A corrected source may still leave stale context
A constructed situation to reason through
Illustrative scenario
- Source corrected
A user changes a fact in the authoritative record.
- Stale recall
An older memory remains in assembled context.
- Diagnostic decision
Separate memory correction, retrieval and answer-generation behaviour.
Use this situation to identify the evidence that would change the decision. The proposed examination must stay within the agreed scope, and any conclusion must describe the conditions actually inspected.
Scope and next decision
Component evidence needs deployment-level limits
Prepare a bounded next discussion
- Memory lifecycle
- Bring one concrete workflow question and its accountable owner.
- Context assembly
- Identify the evidence and access that can be made available.
- Orchestration state
- Agree the specialist involvement and responsibilities required.
- Written scope
- Confirm activities, evidence, readiness and acceptance before commitment.
Component results apply to the tested deployment and conditions. They do not certify a memory provider or prove every workflow safe. Privacy-sensitive tests need authorised data handling, and proposed engineering changes require their own verification.
Explore the discipline
Specialist areas within this capability
Choose a question to explore its methods, boundaries and indicative outputs.
- Memory accuracy and relevance testing
- Memory retention and forgetting evaluation
- Cross-user memory leakage testing
- Memory poisoning assessment
- Temporal consistency evaluation
- Memory provenance and lineage review
- Memory deletion and correction testing
- Long-term personalization evaluation
- Memory cost and retrieval performance testing
- Episodic versus semantic memory comparison
- Context relevance evaluation
- Context-window utilization testing
- Context poisoning and injection assessment
- Retrieval quality evaluation
- Permission-aware retrieval testing
- Citation and grounding evaluation
- Context compression testing
- Stale-context detection
- Context-conflict resolution testing
- Lost-in-the-middle evaluation
- Workflow-routing evaluation
- Planner quality assessment
- Delegation correctness testing
- Multi-agent handoff evaluation
- Loop and deadlock detection
- Termination-condition testing
- Retry and compensation testing
- Workflow determinism assessment
- Supervisor-agent evaluation
- Failure-propagation analysis
- Tool-selection accuracy
- Tool-argument correctness
- Unauthorised tool-use testing
- Partial-execution recovery
- Idempotency evaluation
- Tool-result interpretation
- Connector reliability testing
- Tool-description poisoning tests
- High-impact action confirmation
- Tool-chain integrity evaluation
- Agent context system evaluation
- Agent memory system evaluation
- Agent orchestration system evaluation
Related services
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AI Agent Memory Assurance
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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.