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A relevance score can show whether selected context appears related to a query. It cannot show that an agent handoff worked. To evaluate a handoff, test whether the receiving agent gets the critical facts, constraints, and current state it needs to complete its next task—and whether it uses them correctly.
What a relevance score can—and cannot—tell you
Relevance depends on the information need, not merely on whether text shares words or topics with a query. A score is useful only in relation to a defined task: context that is relevant to a broad topic may still be useless for the specific action the next agent must perform. The information-retrieval textbook explains relevance in relation to an information need.
A handoff is a workflow boundary. OpenAI’s quickstart demonstrates a triage agent handing work to specialist agents, which illustrates a routing pattern—not a guarantee that the receiving agent has enough context or will produce a correct result. OpenAI’s handoff documentation describes that pattern.
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Start with the receiving agent’s actual assignment. For each test case, record the user’s information need, the sender’s payload, the receiver’s task, the details that must survive transfer, freshness expectations, and the expected outcome. This makes it possible to distinguish “the payload looked relevant” from “the receiver could do the required next step.”
- Task completion: Did the receiver perform its assigned next step correctly?
- Critical-fact retention: Did every explicitly required fact and constraint make it through?
- Context precision: Did irrelevant material distract the receiver or prompt unsupported conclusions?
- Freshness: Were old facts identified or excluded when the task required current information?
- Contract compliance: Did the transferred payload match the receiver’s expected input schema?
- Latency and cost: What time and expense did filtering or scoring add?
These dimensions should remain visible rather than being collapsed into one score without an explicit explanation of the weighting and trade-offs.
Build a representative handoff test set
Use a small set of realistic cases drawn from the workflow. Include ordinary successful transfers as well as cases designed to reveal failure. A practical test matrix is an evaluation recommendation, not a universal published standard.
- Missing required fact: Omit a detail the receiver needs and check whether it notices the gap instead of guessing.
- Stale context: Include outdated information and test whether the receiver flags it or relies on it.
- Topically similar distraction: Add related but irrelevant material and see whether it changes the answer or action.
- Low-salience essential detail: Include a small but important constraint that a relevance filter might discard.
- Ambiguous reference: Transfer a phrase such as “that account” or “the earlier result” without a clear referent and check whether the receiver resolves the ambiguity safely.
- Invalid payload: Break a required field or schema rule and verify that the receiver rejects or handles the input as intended.
- Budget pressure: Make the payload too large for the receiving agent’s context budget and test whether essential content is preserved.
The practitioner Inference Systems prompt playbook discusses risks including over-filtering, stale tool results, poor fit with downstream task contracts, ambiguous references, token-budget overruns, and filtering overhead. Its proposed mitigations—such as retention requirements, timestamps or time-to-live rules, schema constraints, and budget checks—are implementation suggestions, not independently validated guarantees.
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Use deterministic checks wherever the expected result is exact. For example, code can verify required fields, allowed values, timestamps, and schema validity. Semantic judgments—such as whether the receiver understood a constraint or completed a research step correctly—may need a rubric or model grader.
OpenAI’s evaluation guidance and API reference document string-check, text-similarity, model-based, and code graders. Anthropic notes that autonomous, flexible agents are harder to evaluate and recommends combining grader types for research-agent evaluations in its agent-evaluation guidance. Neither approach means one grader is sufficient for every workflow. Validate graders against human-reviewed examples, especially for consequential decisions, and use the grader that fits the criterion rather than applying a single method to all outputs.
Compare relevance-only gating with full handoff evaluation
| Evaluation lens | Relevance-only gate | Handoff evaluation |
|---|---|---|
| Task success | Does not establish whether the receiver completed its next task. | Checks the receiver’s actual result against the expected outcome. |
| Critical-fact retention | May favor generally relevant material while dropping an essential detail. | Checks each required fact and constraint explicitly. |
| Irrelevant context | May pass topical material that distracts from the task. | Measures whether irrelevant carryover affects the receiver. |
| Freshness | A high relevance score does not establish that context is current. | Tests whether stale information is flagged or excluded as required. |
| Schema compliance | Does not show that the payload matches the receiver’s input contract. | Validates required fields and input-format rules. |
| Latency and cost | Can measure the gate’s overhead, but not whether it is worthwhile by itself. | Measures overhead alongside any observed improvement in downstream quality. |
A richer evaluation is useful only if its measured quality gain justifies its added latency and cost in the workflow being tested. Keep those operational results alongside quality measures rather than treating a higher relevance score as proof of value.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Interpret multi-agent performance claims narrowly
Anthropic reports that a multi-agent system with Claude Opus 4 as lead and Claude Sonnet 4 subagents outperformed single-agent Claude Opus 4 by 90.2% on Anthropic’s internal research evaluation. The company’s account of that system makes this a result about the described setup and internal evaluation—not a general improvement to expect from adding agents, and not evidence that any particular handoff is reliable.
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