A relevance score can show whether selected context appears related to a query. It cannot show whether the next agent received the facts, constraints, and current state needed to complete its task. Test the handoff at the workflow boundary: evaluate what the receiving agent can actually do with the transferred payload.
Why relevance alone cannot validate a handoff
Relevance is relative to an information need, not merely a match between words in a query and words in a document. A high score can therefore describe context that is topically related while omitting a critical requirement or including material that is stale. Define the receiver’s task before deciding what context counts as useful. See the Information Retrieval textbook for the underlying relevance concept.
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A handoff is also a workflow boundary. OpenAI’s Agents SDK quickstart demonstrates a triage agent routing work to specialists. That illustrates a routing pattern; routing successfully does not, by itself, guarantee the specialist has enough accurate context to do its work.
What a useful handoff evaluation measures
Build a small, representative set of handoff cases. For each, record the user’s information need, the sender’s payload, the receiver’s next task, the details that must be preserved, freshness expectations, and the expected outcome. Score the dimensions separately so a strong result on one does not conceal a failure on another.
#1 Best Overall
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| Dimension | What to check |
|---|---|
| Task completion | Did the receiving agent correctly perform its assigned next step? |
| Required information retention | Did every explicitly critical fact and constraint reach the receiver? |
| Context precision | Did irrelevant material distract the receiver or prompt unsupported conclusions? |
| Freshness | Were stale facts identified or excluded when the task required current information? |
| Contract compliance | Did the transferred payload satisfy the receiver’s required input schema? |
| Latency and cost | How much delay and expense did filtering or scoring add, and was the benefit worth it? |
Do not collapse these results into one score unless you disclose how dimensions are weighted and what trade-offs that weighting makes. A relevance-only gate and a richer handoff test should be compared on the same cases using task success, critical-fact retention, irrelevant-context carryover, freshness, schema compliance, latency, and cost.
How to build a practical test set
- Write the receiver’s job in outcome terms. Specify the next action and what a correct result looks like, rather than asking only whether the payload is relevant.
- Mark indispensable details. Identify required facts, constraints, references, and state. Make exact fields machine-checkable where possible.
- Define freshness and contract requirements. Set expectations for timestamps or time-to-live, reference clarity, schema fields, and token budget.
- Include normal and adversarial cases. Test ordinary transfers alongside missing facts, stale results, distracting but topically similar context, ambiguous references, and malformed payloads.
- Record quality and operational overhead. Track the outcome alongside the additional latency and cost introduced by context filtering or scoring.
These are practical evaluation recommendations, not a published universal standard. The Inference Systems prompt engineering playbook discusses risks such as over-filtering low-salience details, stale tool results, unclear transferred references, receiver token limits, and filtering overhead. Its mitigations—including retention requirements, timestamps or TTLs, schema constraints, token-budget checks, and measuring recall and precision—are implementation suggestions, not independently validated guarantees.
Rank #2
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- Advanced Features: Equipped with 2 GB LPDDR4 RAM, 16 GB eMMC built-in storage, ideal to develop in PC-connected mode, running the OS, Python scripts, and basic network services (SSH) without a demanding GUI or heavy multitasking; great for lightweight AI and memory-optimized TinyML applications, needing local storage for basic OS and core libraries. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
- Intended Use & Development: The perfect platform for prototyping robotics or IoT projects, empowering innovators with a unified development experience to mix Arduino Sketches, Python scripts, and containerized AI models in a single interface.
Choose graders for the criterion
Use deterministic checks for exact fields, required values, and schemas. Use a rubric or model grader when a judgment is semantic, such as whether the next step answered the user’s need. OpenAI’s evals grader reference documents string-check, text-similarity, model-based, and code graders. Anthropic recommends combining grader types for research-agent evaluations because agent autonomy and flexibility make evaluation harder; see its guide to evaluating AI agents. Validate graders against human-reviewed examples, and do not assume one grader fits every criterion.
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Keep performance claims in context
Anthropic reports that a particular multi-agent system—with Claude Opus 4 as lead and Claude Sonnet 4 subagents—outperformed single-agent Claude Opus 4 by 90.2% on the company’s internal research evaluation. That is a company-reported result for that system and evaluation, not a general improvement estimate or proof that adding agents will help another workflow. The Anthropic account of its multi-agent research system provides the stated context.
Rank #3
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- Built in Micro's self-developed 4th generation NPU, with high computational accuracy and support for mixed quantization of int4, int8, and int16. Among them, int8 has a computing power of 0.5 TOPS and int4 has a computing power of up to 1.0 TOPS
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- It has powerful encoding performance, supports intelligent encoding, adapts to save bit rates according to the scene, and saves more than 50% of the bit rate compared to conventional CBR mode, making the captured images high-definition, smaller in size, and doubling the storage space
- The design with built-in RISC-V MCU supports low-power fast startup, 250ms fast capture, and simultaneous loading of AI model library, enabling facial recognition to be completed within 1 second
When evaluating a specific framework or routing design, describe the workflow under test as well as the framework. A handoff result belongs to that task, payload, receiver, and evaluation setup; it should not be treated as a universal ranking of agent systems.
Quick Recap
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