
Evaluating AI Agents: Why Task Success Beats Benchmarks
Public leaderboards reward the wrong things for agentic work. Here's a practical eval harness built around real task completion and side-effect safety.

Public leaderboards reward the wrong things for agentic work. Here's a practical eval harness built around real task completion and side-effect safety.

Why LLMs should generate and execute formulas instead of hallucinating math — plus concrete patterns for verifiable spreadsheet automation.

Stuffing a million tokens into a prompt degrades reasoning more than it helps. The real skill is curating what the model actually attends to.

A practical decision framework for choosing retrieval, full-context, or hybrid approaches based on data volatility, cost, and accuracy.

Practical patterns for coaxing consistent, spreadsheet-ready data out of LLMs — schema enforcement, validation loops, and the pitfalls that quietly corrupt your tables.

Pure vector search over personal files stumbles on recency, permissions, and exact terms. Hybrid retrieval plus metadata is the fix.

Final-answer accuracy hides how agents actually work. Here's why trajectory, tool-use, and recovery metrics matter — plus a practical scoring rubric.

Benchmark scores won't tell you if an email agent is safe to trust. Here's a practical eval harness built on task completion and harm metrics.

Single-turn benchmarks miss what matters. Here's a practical eval harness for agents that move data across email, docs, sheets, and calendar.

A practical framework for routing tasks to small, fast models or frontier reasoning models based on latency, cost, and failure cost.

A quantitative look at how context window limits quietly degrade agent performance on email threading and document synthesis—and what to do about it.

Stop defaulting to the largest frontier model. Use a cost-latency-quality matrix to route each task to the right LLM and cut spend without hurting output.
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