{"product_id":"ai-agent-harness-design","title":"Agent Harness \u0026 Eval Design","description":"\u003ch2\u003eAgent Harness \u0026amp; Evaluation Design\u003c\/h2\u003e\u003cp\u003eAgents fail differently than traditional software: non-deterministic outputs, tool-call side effects, and context drift make \"it worked in the demo\" a poor quality bar. Techwall's \u003cstrong\u003eAgent Harness \u0026amp; Eval Design\u003c\/strong\u003e service gives you a repeatable way to measure agent quality before release and after every change.\u003c\/p\u003e\u003cp\u003eWe design harnesses that fit your release process — whether you ship weekly prompts, monthly model upgrades, or continuous deployments to thousands of users.\u003c\/p\u003e\u003ch3\u003eWhy Eval Harnesses Matter\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRegression safety\u003c\/strong\u003e — catch broken tool schemas and policy violations before users do.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eModel migration\u003c\/strong\u003e — compare GPT, Claude, or open-weight models on the same scenarios.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCost control\u003c\/strong\u003e — benchmark token use and latency per workflow.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompliance evidence\u003c\/strong\u003e — documented test results for internal audit and customer trust.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eWhat We Build (Design + Implementation Guidance)\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eScenario libraries covering happy paths, edge cases, and known failure modes\u003c\/li\u003e\n\u003cli\u003eAutomated runners integrated with GitHub Actions, GitLab CI, or your pipeline\u003c\/li\u003e\n\u003cli\u003eTracing hooks (OpenTelemetry-compatible patterns) for production vs eval parity\u003c\/li\u003e\n\u003cli\u003eHuman review queues for subjective or high-risk outputs\u003c\/li\u003e\n\u003cli\u003eDashboards: pass rate, cost per eval run, drift over time\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eOur Process\u003c\/h3\u003e\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eInventory\u003c\/strong\u003e — catalog agent capabilities, tools, and business-critical outcomes.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMetric design\u003c\/strong\u003e — define what \"good\" means (accuracy, safety, completion rate, user satisfaction proxy).\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHarness architecture\u003c\/strong\u003e — choose assertion types, dataset format, and environment isolation.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePilot suite\u003c\/strong\u003e — 20–50 scenarios that block bad releases.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eScale-up\u003c\/strong\u003e — expand coverage, schedule runs, tie to \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eFDE\u003c\/a\u003e rollout checkpoints.\u003c\/li\u003e\n\u003c\/ol\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eEval harness specification and repository structure\u003c\/li\u003e\n\u003cli\u003eInitial scenario dataset (JSON\/YAML) with expected behaviors\u003c\/li\u003e\n\u003cli\u003eCI integration guide and quality gates\u003c\/li\u003e\n\u003cli\u003eObservability map: what to log in dev vs production\u003c\/li\u003e\n\u003cli\u003eModel upgrade playbook\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003ePairs With Agent Design\u003c\/h3\u003e\u003cp\u003eBest results come when \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e defines tool contracts and success metrics upfront — we embed eval hooks into the architecture. Already have a live agent? We reverse-engineer scenarios from logs and incident history.\u003c\/p\u003e\u003ch3\u003eRelated Services\u003c\/h3\u003e\u003cp\u003e\u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e · \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eForward Deployed Engineering\u003c\/a\u003e · \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e\u003c\/p\u003e\u003ch3\u003eFrequently Asked Questions\u003c\/h3\u003e\u003cp\u003e\u003cstrong\u003eDo you use LLM-as-judge for evaluations?\u003c\/strong\u003e\u003cbr\u003eSometimes — we combine LLM judges with deterministic checks on tool arguments, JSON shape, and policy rules to reduce false positives.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan you eval agents that call live APIs?\u003c\/strong\u003e\u003cbr\u003eYes. We design sandbox mocks, recorded fixtures, and staged environments so evals stay fast and safe.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWe use LangSmith \/ Braintrust \/ custom tools — can you work with that?\u003c\/strong\u003e\u003cbr\u003eYes. The harness design is tool-agnostic; we document integration patterns for your stack.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eHow is this different from QA testing?\u003c\/strong\u003e\u003cbr\u003eTraditional QA assumes deterministic outputs. Agent evals measure distributions, tool correctness, and safety over many runs — closer to ML ops than manual test scripts.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat if our agent is embedded in hardware?\u003c\/strong\u003e\u003cbr\u003eWe design edge-aware evals including latency budgets and offline fallbacks — see \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e.\u003c\/p\u003e\u003cscript type=\"application\/ld+json\"\u003e{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Do you use LLM-as-judge for evaluations?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"We combine LLM judges with deterministic checks on tool arguments, JSON shape, and policy rules to reduce false positives.\"}},{\"@type\":\"Question\",\"name\":\"Can you eval agents that call live APIs?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. We design sandbox mocks, recorded fixtures, and staged environments so evals stay fast and safe.\"}},{\"@type\":\"Question\",\"name\":\"Can you work with LangSmith or Braintrust?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. The harness design is tool-agnostic; we document integration patterns for your stack.\"}},{\"@type\":\"Question\",\"name\":\"How is agent eval different from QA testing?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Agent evals measure distributions, tool correctness, and safety over many runs — closer to ML ops than manual test scripts.\"}},{\"@type\":\"Question\",\"name\":\"Can you eval agents embedded in hardware?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. We design edge-aware evals including latency budgets and offline fallbacks.\"}}]}\u003c\/script\u003e","brand":"Tech Wall Electronics","offers":[{"title":"Default Title","offer_id":46479071805576,"sku":null,"price":0.0,"currency_code":"HKD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0641\/6692\/0328\/files\/Artificial_Intelligence_AI_Development_Service_-_AI_Agents_a666078b-fde8-4412-9fb9-65d0d80b30c1.png?v=1788428295","url":"https:\/\/www.techwall.com.hk\/products\/ai-agent-harness-design","provider":"Tech Wall Electronics","version":"1.0","type":"link"}