The Tool Desk
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Verify a two-agent interaction as a contract-backed execution trace, not as a convincing conversation. Define the protocol and success conditions first; record every message, model call, tool action, state change and failure; then run deterministic checks for schemas, ordering, authorization, acknowledgements, termination and final state. For concurrent or safety-critical workflows, model-check the coordination logic and replay the resulting counterexamples. Finally, evaluate the task outcome independently with executable domain checks and, where necessary, a human or language-model rubric. A passing report is only as strong as its stated assumptions, model and tool versions, test schedule and coverage: it proves the checked contract in the tested conditions, not universal correctness of a stochastic language model.
What does it mean for two AI agents to complete an interaction correctly?
“Correct” has two separate dimensions. The agents must follow the interaction protocol, and their work must produce the required real-world result. A fluent final message can satisfy neither: agents may skip an approval, call a tool with the wrong argument, lose a handoff, or report success while the target database remains unchanged.
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Write correctness as a contract with explicit, machine-checkable rules:
- Roles and identities: which agent may initiate, approve, call each tool or close the task.
- Message vocabulary: permitted performatives or message types and required fields.
- Preconditions: facts that must hold before a message, tool call or state transition.
- Postconditions: facts that must hold after each action and at termination.
- Ordering and handoffs: which events must precede others, including required acknowledgements.
- Failure behavior: retry limits, timeouts, cancellation, compensation and escalation.
- Terminal states: the exact conditions for success, rejection, abort or partial completion.
- Task-level success: an independently observable result such as a validated record, calculation or policy decision.
Protocol and interoperability research treats compliance as a property that can be specified and checked. The Message Agent Protocol (MAP) literature describes formal message-exchange patterns and verification techniques in its Journal of Applied Logic paper and an associated treatment of interaction protocols at NC State University.
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How do I write a contract before running the agents?
Start with a state machine or transition table rather than a prose prompt. Name every state, legal transition, actor, required input and resulting state. Include negative rules, because “must not” conditions often expose failures that a happy-path script misses.
Example contract structure
| Contract element | What to specify | Example check |
|---|---|---|
| Message | Type, sender, recipient, required fields and correlation ID | REQUEST contains a task ID and authorized sender |
| Precondition | State and permissions required before an action | Agent B may execute only after a verified request |
| Transition | Allowed next state and event | REQUESTED → VERIFIED only after a verification result |
| Postcondition | Observable effect that must follow | The referenced record contains the committed value |
| Forbidden edge | Action that is never legal | No final answer before peer acknowledgement |
| Terminal rule | Conditions for success, failure or cancellation | Success requires both acknowledgement and final-state assertion |
Specify how retries interact with idempotency, whether duplicate messages are ignored or processed, and what happens if an agent restarts with incomplete state. Put the contract under version control. The version identifier must travel with every execution trace so a later reviewer can tell which rules were applied.
MAP provides a formal language for message-exchange patterns, while interaction-type research frames compliance and interoperability as properties that can be verified. Those ideas are useful even when your implementation uses ordinary JSON messages: the contract should be precise enough that a program can reject an illegal transition without asking a language model for an opinion.
What must a complete execution trace contain?
The trace is your evidence. Persist an append-only event for every meaningful step, not just the final transcript. A practical event record includes:
- event ID, correlation ID and logical step number;
- wall-clock timestamp and, for distributed systems, a logical or monotonic clock;
- agent identity, model identifier and runtime version;
- input, output and message type, with hashes when retaining raw content is inappropriate;
- sender, recipient and conversation or task identifier;
- tool name, arguments, authorization result, return value or error;
- state before and after each mutation;
- retry count, timeout, cancellation and restart events;
- contract, prompt, tool-schema and policy versions;
- termination decision and the observed final state.
Capture receives as well as sends. A send-only log cannot establish that the peer received, interpreted or acknowledged a message. Record model invocations even when they produce no outward message, because an unlogged hidden call may explain a later state change.
The trace-assurance framework calls these records Message-Action Traces and describes step and trace contracts plus deterministic replay in its paper. Open Agent Specification tracing likewise represents execution paths, data flow, decisions and tool invocations so different runtimes can be compared; its cross-framework study covers four runtimes and three benchmarks (ACM CAIS paper).
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Make replay possible
Store the exact serialized message and tool arguments, not an informal rendering. Keep deterministic fixtures for external tools or record their responses so a replay does not silently query a changed service. Preserve model and tool versions, random seeds when available, scheduling choices and relevant environment configuration. Redact secrets at collection time, but retain a cryptographic digest or a classified placeholder that lets an auditor detect later alteration.
Which checks should run before judging language quality?
Run executable trace predicates first. They are faster, reproducible and less vulnerable to a persuasive but incorrect answer than a general language-model judge.
| Check family | What to validate | Useful failure evidence |
|---|---|---|
| Schema and identity | Message type, required fields, sender, recipient and correlation ID | First malformed event and the raw payload |
| Ordering and state | Legal transition, step number, duplicate handling and current state | Expected state versus observed state at the violating event |
| Authorization | Agent role, tool permission and policy decision | Tool call, principal and authorization result |
| Arguments and results | Tool name, argument types, constraints and result consumption | Validated argument diff and subsequent state |
| Handoff | Required acknowledgement, completeness marker and recipient acceptance | Missing or mismatched acknowledgement IDs |
| Reliability rules | Timeout, retry, cancellation, duplicate and restart behavior | Retry history and elapsed times |
| Forbidden edges | No skipped verification, unauthorized action or premature final answer | Counterexample path from the last legal state |
| Terminal state | Success or failure predicate and task-level assertions | Final-state snapshot and unmet assertions |
Keep the first violation and its counterexample trace. Reporting only a final “fail” forces investigators to guess where the contract was broken and can hide cascading errors. AgentPex illustrates specification extraction and evaluation across output, transition, forbidden-edge and argument rules. Its transparency note also explains why an LLM-based evaluator is complementary rather than a definitive correctness oracle.
When should I use model checking for the coordination protocol?
Use formal model checking when agents are asynchronous, share resources, can retry or restart, or control safety-sensitive actions. A trace checker tells you what happened in one run; a model checker explores the abstract protocol’s possible interleavings within its stated bounds.
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- Safety invariants: an unauthorized agent never commits a change; a resource is never allocated twice.
- Progress: every accepted request eventually reaches a response or an explicit failure state.
- Termination: a dialogue cannot continue forever through retries or circular handoffs.
- Deadlock freedom: no reachable state leaves every agent waiting for an event that cannot occur.
- Livelock freedom: agents do not exchange valid-looking messages indefinitely without completing useful work.
PROMELA/SPIN and PlusCal/TLA+
Translate the coordination logic—not the full language model—into a finite abstraction. Model message queues, ownership, timeouts, retries and shared resources; represent model output as nondeterministic choices constrained by the contract. PROMELA/SPIN is suited to communicating processes and explicit counterexample traces. PlusCal/TLA+ expresses state transitions and temporal properties; TLC can search bounded state spaces and return a violating execution.
TraceFix describes a pipeline that synthesizes a topology, generates PlusCal, uses TLC counterexamples for repair and enforces the verified topology at runtime (project page). MAP research also describes translating dialogue protocols to PROMELA and checking termination, liveness and correctness (Journal of Applied Logic). As its authors put it: “By performing model checking on our protocols we can obtain a high degree of confidence in the resulting agent dialogues.”
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A model-checking pass does not certify every natural-language response. It certifies the modeled states, transitions and assumptions. Record the abstraction, queue and state bounds, fairness assumptions and any behavior intentionally left out.
How do I verify that the final task outcome is actually correct?
Evaluate the outcome independently from the conversation. Prefer executable assertions tied to the domain:
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- query the resulting database or file and compare it with the required schema and values;
- recompute arithmetic, totals or transformations from source inputs;
- validate policy decisions against a versioned rule set;
- check citation entailment or required fields with deterministic validators where possible;
- compare a generated artifact with a reference answer using domain-specific tolerances;
- ask a qualified human to inspect cases whose correctness cannot be reduced to a predicate.
Keep the outcome verdict separate from the protocol verdict. A run can be protocol-compliant but produce a wrong result because the contract was incomplete; it can also produce the right result while violating authorization or handoff rules. Neither verdict cancels the other.
What role can an LLM judge play?
Use a language-model judge for qualities such as relevance, explanation quality or nuanced policy interpretation that lack reliable executable tests. Give it the trace, contract version and a structured rubric, and record its rationale and uncertainty. Do not let it override a failed deterministic assertion.
MAS-ProVe evaluated LLM-as-a-Judge, reward models and process reward models across six diverse multi-agent frameworks and four context-management strategies. Its 2026 results report that process-level verification “does not consistently improve performance and frequently exhibits high variance” (paper). The authors conclude that robust process verification remains an open challenge. Treat a judge as corroborating evidence, not sole proof.
How should I test nondeterminism, faults and adversarial conditions?
One successful replay is not a reliability claim. Execute the same contract and task across model versions, random seeds, temperatures, schedules and retry policies. Compare both verdicts and traces, not only the final answer.
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Inject realistic failures
- delay, duplicate, reorder, drop or corrupt messages;
- return malformed, stale, contradictory or partial tool results;
- force authorization denials, rate limits and timeouts;
- restart an agent between send and acknowledgement;
- remove or corrupt shared state and test recovery;
- insert untrusted instructions or adversarial context into an otherwise valid task;
- stop one agent after partial completion and verify cancellation or compensation.
Measure task success, contract compliance, termination reliability, factuality indicators, containment of unauthorized actions, governance outcomes, latency and resource use. The trace-assurance work at arXiv explicitly proposes stress testing, bounded counterexample search and fault injection across these classes.
Define a release threshold
Set thresholds before running the test set: for example, zero safety violations, no unexplained terminal states, and a specified success rate across seeds and injected faults. Keep the test corpus, schedules, seeds, failures and waivers in the report. A threshold is a governance decision, not a universal number supplied by the model or framework.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a verification report contain?
A reviewer should be able to reproduce the verdict and understand its boundary. Include:
- contract and specification version, including every changed rule;
- agent, model, tool, runtime and evaluator versions;
- task corpus, prompts or input hashes, seeds, temperatures and schedules;
- trace-retention and redaction policy;
- each predicate, its pass/fail result and the first counterexample for failures;
- model-checking abstraction, bounds, fairness assumptions and explored states;
- fault-injection matrix and recovery behavior;
- independent outcome checks and any human or LLM-judge rubric;
- latency, token or compute use, observability overhead and repair effort when comparing implementations;
- untested assumptions, known blind spots and the exact scope of the claim.
Use a verdict vocabulary that distinguishes protocol-compliant, outcome-correct, both, failed and inconclusive. “Inconclusive” is appropriate when logging is incomplete, a tool result cannot be replayed or a judge disagrees with an executable check.
How can I compare two agent implementations fairly?
Hold the specification, task set, tool permissions and evaluation harness constant. Compare implementations on several axes rather than a single success percentage.
| Axis | Questions to ask |
|---|---|
| Protocol assurance | Are messages typed? Which safety, liveness, deadlock and termination properties are checked? |
| Evidence quality | Is every event captured, replayable and attributable to an agent and version? |
| Outcome validity | Are results checked against executable domain assertions and independent review? |
| Robustness | How do verdicts change across seeds, schedules, retries, faults, adversarial context and model versions? |
| Operational cost | What latency, token or compute use, observability overhead and repair effort are required? |
| Guarantee scope | What state space, fairness assumptions and environmental conditions were actually modeled? |
Open Agent Specification’s framework-agnostic representation and structured traces are intended for this kind of cross-runtime comparison (ACM CAIS paper). Report raw counts and counterexamples alongside aggregate metrics so a small number of severe violations cannot disappear inside an average.
What can a passing verification result not prove?
A protocol checker proves properties of the protocol model, not every possible behavior of an unconstrained stochastic language model. A bounded model may omit a queue length, schedule or data value that occurs in production. A complete-looking trace can still contain an incorrect external observation, and an LLM judge can agree with a fluent but wrong answer. Even repeated passing runs establish confidence only for the tested contract, versions, inputs, schedules and fault assumptions.
State those boundaries in the report. When the stakes are high, combine runtime enforcement, deterministic assertions, formal analysis, adversarial testing and qualified human review rather than promoting one score to a universal correctness claim.
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What is a practical end-to-end verification sequence?
- Freeze the contract: version roles, messages, preconditions, postconditions, forbidden transitions, retries, timeouts and terminal states.
- Instrument before deployment: emit durable events for sends, receives, model calls, tools, state mutations, retries, errors and termination.
- Validate each run: execute schema, identity, ordering, authorization, argument, handoff and terminal-state predicates.
- Preserve failures: stop at the first violation, save the counterexample trace and classify the failure.
- Model coordination: for concurrency or high-impact tasks, check the abstract protocol in PROMELA/SPIN or PlusCal/TLA+.
- Assert the outcome: query the resulting state, recompute values and apply domain-specific validators independently.
- Stress the system: vary seeds, schedules and versions; inject message, tool, state and restart faults.
- Review and report: reconcile protocol and outcome verdicts, document assumptions and publish reproducible evidence.
Frequently Asked Questions
Can I verify an interaction if I did not record the raw prompts?
You can still check some structure using event metadata, message hashes, schemas and state snapshots, but you cannot fully replay or inspect content-dependent decisions. Mark the run as limited or inconclusive for any property that requires the missing inputs, and improve instrumentation before the next execution.
Should timestamps or logical step numbers be the source of truth for ordering?
Use logical step numbers and correlation relationships for protocol legality, with wall-clock or monotonic timestamps for latency and timeout analysis. Distributed clocks can be skewed, so a timestamp alone should not establish that one agent observed another event.
What is the safest way to handle a disagreement between a judge and an executable check?
Keep both results and classify the run as disputed rather than averaging them. Inspect the predicate, input data and trace, then have a domain owner decide whether the executable rule, the judge rubric or the contract needs correction.
How often should a multi-agent contract be re-verified after a change?
Re-run deterministic checks and the relevant model-checking bounds whenever roles, message schemas, tools, retries or terminal conditions change. Also rerun the fault and seed matrix when model, runtime, scheduler or external-state behavior changes, because those changes can invalidate prior evidence.
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