Tool-argument correctness
Determine whether arguments preserve the user's intended entities and constraints
When tool calls fail or affect the wrong records despite valid syntax, this area becomes relevant. For the agent integration and quality owner, the concern is specific: a syntactically valid argument refers to the wrong account or unit. The deployment evaluation starts from that consequence and follows the relevant evidence through the customer workflow. Schema validation is one layer of the test. The proposed evaluation should also examine business meaning: identity, units, dates, scope, and constraints that can be wrong even when the request is structurally accepted.
Connect the operating question to inspectable evidence
- Agent integration and quality owner
Owns the bounded decision and the consequence of acting.
- Tool schemas
Defines the starting context for the proposed work.
- Entity resolution
Exposes the first material uncertainty to examine.
- Validation and clarification recommendations
Carries unresolved conditions into an accountable next action.
Decision at stake
Determine whether arguments preserve the user's intended entities and constraints
Make the decision explicit
- Agent integration and quality owner
- Tool calls fail or affect the wrong records despite valid syntax.
- Decision
- Whether arguments preserve the user's intended entities and constraints.
- Entity resolution
- Use tool schemas to establish the relevant starting condition.
For the agent integration and quality owner, this becomes relevant when tool calls fail or affect the wrong records despite valid syntax. The immediate decision is whether arguments preserve the user’s intended entities and constraints. 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 a syntactically valid argument refers to the wrong account or unit. 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 entity resolution alongside required constraints
Keep the review distinctions visible
- Entity resolution
- Bind names and references to the intended entity before constructing payloads.
- Required constraints
- Retain requested bounds, recipients and amounts in required arguments.
- Type and semantic correctness
- Check units and meaning even when the payload satisfies the schema.
Entity resolution
For this scope, entity resolution should be examined alongside tool schemas. Test entities with similar names and different identifiers. The agent should establish which record the user intends before preparing a consequential call. A structurally valid identifier can still target the wrong account, so the evaluation needs business-level expected entities rather than schema checks alone.
Required constraints
For this scope, required constraints should be examined alongside task-to-argument expectations. Vary dates, units, quantities, and enumerated meanings within valid schemas. Inspect whether arguments preserve the request’s semantics and required constraints. The proposed cases should make defaults visible, because an omitted optional field can change the business effect even when the API accepts the payload.
Type and semantic correctness
For this scope, type and semantic correctness should be examined alongside entity and unit fixtures. Check the final payload after all transformations and clarifications. The agent may begin with the correct interpretation and lose a constraint later. The result should preserve the requested action, resolved entity, authorised scope, and dispatched arguments so the owner can locate the point of divergence.
Prerequisites
Start with tool schemas
Give each input a specific job
- Tool schemas
- Defines the operating boundary.
- Task-to-argument expectations
- Examine required constraints.
- Entity and unit fixtures
- Shows conditions affecting type and semantic correctness.
- Product or platform owner
- Confirm access and a reviewer who can resolve gaps.
The starting material is tool schemas, task-to-argument expectations, and entity and unit fixtures. 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
- Entity resolution
Use tool schemas to define the decision criteria.
- Required constraints
Inspect task-to-argument expectations and retain observations that challenge the expected result.
- Type and semantic correctness
Use entity and unit fixtures to identify unresolved conditions and a responsible owner.
- Validation and clarification recommendations
Link the recommendation to its evidence, remaining limits and next validation condition.
Create argument fixtures that are structurally valid but semantically wrong. Vary identifiers, dates, units, optional defaults, and scope constraints while keeping the user’s intended action explicit. The proposed tests should inspect the final payload and external target so a successful API response does not hide an incorrect business operation.
A practical first slice would focus on entity resolution and then follow its consequences into required constraints. Keep type and semantic correctness 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
Ambiguous display name: authorised target
Examine the constructed decision boundary
Illustrative scenario
- Ambiguous display name
An update request names a person or record that matches several identifiers.
- Entity resolution
Require disambiguation before constructing the API payload.
- Authorised target
Check the intended entity, not merely identifier syntax; repair lookup or clarification as needed.
Consider an illustrative situation in which an agent prepares a case update using a display name where the API requires an unambiguous record identifier. This is a constructed example, not a client engagement or an observed Softlander outcome.
For a display name that matches several records, an illustrative case should require disambiguation before preparing the update. The expected behaviour is not merely a well-formed identifier but the authorised intended entity. Findings can guide clarification, lookup, validation, or tool schema changes according to the actual failure path.
The useful learning would be the difference between the expected behaviour and the behaviour or responsibility actually demonstrated. If the exercise leaves type and semantic correctness 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 validation and clarification recommendations to make the next action explicit
Every artefact needs an operating use
Illustrative working format
- Argument correctness cases
- Boundary — tool schemas.
- Semantic error findings
- Findings about required constraints.
- Validation and clarification recommendations
- Action on type and semantic correctness.
Indicative outputs include argument correctness cases, semantic error findings, and validation and clarification recommendations. 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 argument correctness cases.
- Product or platform owner
- The customer retains release authority and owns remediation priorities.
- Universal vendor or component assurance
- Cannot be inferred from semantic error 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.