How to Benchmark AI Agent Accuracy: MMLU, MILU, BFCL & Private Evals
To benchmark AI agent accuracy, use public suites (MMLU/MILU for knowledge breadth, BFCL-style tests for tool calling) as coarse filters—then build a private eval set of 100–500 real journeys that scores task success, tool correctness, and policy violations. Buying on leaderboard rank alone ships the wrong agent for banking, healthcare, or collections in India.
What Are MMLU, MILU, and BFCL Good For?
Short answer: Comparing base model competence and tool-use skill—not proving your production agent works.
Public benchmark roles
- MMLU (Massive Multitask Language Understanding): Broad knowledge across dozens of subjects—useful for shortlisting general reasoning ability.
- MILU / multilingual variants: Stress non-English and cross-lingual knowledge—relevant when your agent serves Indic-language markets.
- BFCL-style tool calling: Function name selection, argument formatting, multi-step tool plans—filters models before you wire your own APIs.
They help shortlist models; they do not measure EHR write-back, collections script compliance, or hallucination rate on missing account data.
Why Public Scores Mislead Buyers
Short answer: Leaderboards test isolated model skills—not your prompts, tools, guardrails, or real user noise.
A +5 point MMLU gain does not fix invented policy clauses or a tool schema mismatch. Production accuracy is a system property: model + retrieval + tools + validators + escalation policy. Treat public scores as necessary but insufficient filters.
What Should a Private Eval Set Include?
Short answer: Happy paths, adversarial prompts, language mix, and "must refuse" cases.
Core eval categories
- Happy paths: Gold tool args and expected side effects (ticket created, slot booked)
- Hallucination traps: Missing account data—agent must refuse or escalate
- Policy edges: Requests that must be denied (medical advice, unauthorized refunds)
- Language mix: Hinglish, Tanglish, and code-switched samples for your markets
- Adversarial: Prompt injection, jailbreak attempts, social-engineering scripts
Private eval scoring rubric
- Task success: Did the user goal complete correctly?
- Tool correctness: Right function, valid args, expected DB/API effect
- Policy compliance: Zero violations on "must refuse" cases
- Hallucination: Hard fail on any invented entity—see Reduce hallucinations
- Containment vs escalation: Right decision when stuck
Typical Targets on Private Evals
Short answer: Aim for high task success on happy paths, zero policy violations on refusal cases, and low hallucination rate—exact thresholds depend on risk tier.
- Happy-path task success: often > ~90% before canary
- Tool call accuracy: often > ~95% on schema-valid cases
- Policy "must refuse" suite: typically 100% pass required
- Hallucination rate: often targeted under ~2% on frozen set
These are illustrative targets for planning—not vendor claims.
How Often Should You Re-Run Evals?
Short answer: On every prompt/model/tool change, plus a weekly regression gate.
Freeze a regression subset in CI: any deploy that drops task success beyond your error budget or introduces a policy failure blocks promotion. Pair eval gates with shadow mode on live traffic before canary—evals catch known failures; shadow catches drift and edge cases you did not label yet.
Hub: How to Improve AI Agents · Production: Production-ready · Guardrails: Add guardrails · Latency: Improve latency.
