Agent context system evaluation
Decide which context subsystem gaps materially affect the deployed workflow
When the team needs an integrated view of context-related failures, this area becomes relevant. For the AI product and context platform owner, the concern is specific: several individually plausible context components combine into an unsupported final decision. The deployment evaluation starts from that consequence and follows the relevant evidence through the customer workflow. Begin with an end-to-end failure and use focused component comparisons to locate causes. The scope should include only context dimensions material to the decision, preserving the distinction between retrieval, assembly, model interpretation, and external action.
Connect the operating question to inspectable evidence
- AI product and context platform owner
Owns the bounded decision and the consequence of acting.
- Context lifecycle map
Defines the starting context for the proposed work.
- Selection and grounding
Exposes the first material uncertainty to examine.
- Prioritised deployment improvements
Carries unresolved conditions into an accountable next action.
Decision at stake
Decide which context subsystem gaps materially affect the deployed workflow
Make the decision explicit
- AI product and context platform owner
- The team needs an integrated view of context-related failures.
- Decision
- Which context subsystem gaps materially affect the deployed workflow.
- Selection and grounding
- Use context lifecycle map to establish the relevant starting condition.
For the AI product and context platform owner, this becomes relevant when the team needs an integrated view of context-related failures. The immediate decision is which context subsystem gaps materially affect the deployed workflow. The scope should be narrow enough that the evaluation team can identify a meaningful alternative, including continuing the current approach if the available evidence already supports it.
The practical failure to challenge is that several individually plausible context components combine into an unsupported final decision. That concern gives the work a concrete direction: establish what the relevant boundary should do, inspect how it behaves or would operate, and identify the owner of any gap. It should not be diluted into a broad transformation exercise before the specific question has been answered.
Coverage
Examine selection and grounding alongside trust and permissions
Keep the review distinctions visible
- Selection and grounding
- Trace necessary evidence from retrieval into the grounded task decision.
- Trust and permissions
- Inspect source trust and the user's permissions at assembly boundaries.
- Compression and freshness
- Check that shortening or refreshing context preserves the relevant conditions.
Selection and grounding
For this scope, selection and grounding should be examined alongside context lifecycle map. Begin with tasks that expose whether the context system selects and supports the right information. Inspect retrieval, assembly, and claim support as distinct stages. The proposed evaluation should choose focused component comparisons where they can explain an observed end-to-end failure rather than test every possible context property indiscriminately.
Trust and permissions
For this scope, trust and permissions should be examined alongside retrieval and assembly traces. Map source trust and user permissions across the context lifecycle. The same passage can be relevant yet inappropriate for the acting user or too untrusted to influence an action. The scope should identify where selection, filtering, and interpretation enforce those distinctions and where qualified review is required.
Compression and freshness
For this scope, compression and freshness should be examined alongside task and authority criteria. Include transformations and time changes that affect meaning, such as compression and source replacement. Trace their interaction with final behaviour. The useful system-level finding explains how several individually plausible components produced an unsupported outcome and assigns the next evidence or remediation step to the right owner.
Prerequisites
Start with context lifecycle map
Give each input a specific job
- Context lifecycle map
- Defines the operating boundary.
- Retrieval and assembly traces
- Examine trust and permissions.
- Task and authority criteria
- Shows conditions affecting compression and freshness.
- Product or platform owner
- Confirm access and a reviewer who can resolve gaps.
The starting material is context lifecycle map, retrieval and assembly traces, and task and authority criteria. Review these together rather than assuming one document tells the whole story. A stated rule may differ from a configured behaviour, and a proposed responsibility may not yet have an available owner. Identify those differences before selecting the detailed work.
Participation should include a product or platform decision owner, a technical lead, a data owner, and a competent reviewer for the relevant boundary. The customer should define which information and environments may be used. Synthetic or sanitised examples can support preparation when they preserve the relevant behaviour; if they omit a material dependency, record the limitation and agree how the missing evidence would be obtained.
How the work proceeds
Tie the deployment finding to observable behaviour
Follow the evidence through the work
- Selection and grounding
Use context lifecycle map to define the decision criteria.
- Trust and permissions
Inspect retrieval and assembly traces and retain observations that challenge the expected result.
- Compression and freshness
Use task and authority criteria to identify unresolved conditions and a responsible owner.
- Prioritised deployment improvements
Link the recommendation to its evidence, remaining limits and next validation condition.
Start with representative end-to-end tasks and select component experiments only where they answer a material causal question. The proposed evaluation may inspect retrieval, permissions, assembly, grounding, compression, freshness, and conflict handling, but each selected dimension should connect to the release or remediation decision rather than become an omnibus checklist.
A practical first slice would focus on selection and grounding and then follow its consequences into trust and permissions. Keep compression and freshness visible as a separate review question rather than assuming it is satisfied by the first result. The evaluation team should record observations, competing explanations, and changes to the original assumptions. A second reviewer can challenge whether the evidence supports the recommendation and whether an apparently successful example omitted the condition that matters most.
Illustrative scenario
Old policy loses its date: prioritised repairs
Examine the constructed decision boundary
Illustrative scenario
- Old policy loses its date
A support agent compresses a superseded policy and cites it as current.
- Four-stage comparison
Inspect retrieval, assembled context, compressed text and final citation.
- Prioritised repairs
Assign contributing failures by observed consequence while preserving untested interaction limits.
Consider an illustrative situation in which a support agent retrieves a superseded policy, compresses away its date, and cites it as current. This is a constructed example, not a client engagement or an observed Softlander outcome.
For the superseded compressed policy, an illustrative analysis would compare the original retrieval result, assembled context, compressed form, and final citation. This could reveal several contributing failures with different owners. The output should prioritise changes by observed consequence and evidence strength, preserving uncertainties about interactions that were not tested.
The useful learning would be the difference between the expected behaviour and the behaviour or responsibility actually demonstrated. If the exercise leaves compression and freshness unresolved, that should remain a named next step. A recommendation to narrow the scope, improve a prerequisite, or hold the decision can be appropriate when the example exposes a material dependency.
Indicative artefacts
Use prioritised deployment improvements to make the next action explicit
Every artefact needs an operating use
Illustrative working format
- Context system evaluation plan
- Boundary — context lifecycle map.
- Cross-component findings
- Findings about trust and permissions.
- Prioritised deployment improvements
- Action on compression and freshness.
Indicative outputs include context system evaluation plan, cross-component findings, and prioritised deployment improvements. Their form should follow the decision and the customer’s existing operating records. The first should establish the assessed boundary and relevant observations; the second should make material gaps or dependencies inspectable; the third should identify the action, owner, and evidence needed to revisit the decision.
Acceptance should be tested through use: can the responsible owner explain what was examined, identify an unresolved issue, and act on the next step without reconstructing the whole engagement? Findings should preserve counterevidence and limits. A polished document is insufficient if it does not help resolve the customer’s specific question.
Scope and authority
A deployment finding remains bounded to the tested conditions
Keep analytical work separate from decision authority
- Authorised deployment cases and traces
- Can include context system evaluation plan.
- Product or platform owner
- The customer retains release authority and owns remediation priorities.
- Universal vendor or component assurance
- Cannot be inferred from cross-component findings.
The proposed evaluation concerns the named deployment, configuration, cases, and authorised environment. Findings do not certify a component vendor, establish universal safety, or approve release. Unexamined provider paths and specialist questions remain explicit limitations.
Ways to engage
Related engagement scopes
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 Agent Component Evaluation
Find where agent behaviour breaks down. Follow memory, retrieval and delegation through your workflow, then isolate the components that need attention.
Continue exploring
Related insights and resources
Engineer the harness, not just the prompt
Define the contracts around an agent's decisions: what state it trusts, which actions can execute, how interrupted work resumes, and what evidence an operator needs to recover safely.
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.
The evidence behind the go-live decision
A working paper for leaders who need to explain why an agent workflow is ready for its next release. Includes a proposed evidence-pack structure, decision table and review agenda.