#Agent Evaluation

Record the inputs, tools and environment behind an AI agent run. Separate trace playback from fresh execution with a practical reproducibility worksheet.

Assess Cognition SWE-2 for coding work. Check Devin access, the limited free-use offer, benchmark scope and review costs before choosing a paid plan.

Select AI tool results without losing evidence. Use a field-level reference for identifiers, errors, summaries and artifacts that agents can retrieve later.

Assess rising AI usage against accepted work, review effort and delays. Build an evidence record before expanding access or claiming team productivity gains.

Build an interactive product demo with coding agents. Define one user journey, label simulated behavior and test a resettable experience before showing it.

GPT-Live-1 separates live conversation from backend work. Compare delegation modes, duration pricing and pilot checks before choosing a business voice setup.

Map managed agent state across conversations, compute and business actions. Check what survives a restart with a responsibility table and recovery worksheet.

OpenAI Agents API moves the agent loop into a managed runtime. Compare environment choices, recovery limits and application duties before planning a migration.

Keep voice conversations useful during slow tool calls. Design progress updates, corrections and late-result handling around the task’s verified backend state.

Measure voice agent latency from speech to verified results. Use a timing dictionary to separate first audio, tool delays, playback and interruption recovery.

Defining ASR for production agents — completion vs partial vs hallucinated, cost-adjusted scoring, and benchmark suites. Formal methodology + reference dataset.