Observability

TokTier targets a hidden agent-serving bottleneck: repeatedly tokenizing long transcripts after small tool results. Its stateful repair cut median vLLM time to first token by 16–34% in reported tests.

AI Gateway can now enforce spend caps across a team, project, or API key, giving agent workloads layered cost controls instead of relying on per-key limits alone.

Boundary replaces blanket code review with stable architecture rules, agent-generated tests, transcript inspection, execution traces, and human triage—a concrete harness pattern, not a license to skip verification.

AI Gateway’s dedicated logs expose per-request cost, tokens, latency, routing, and provider fallbacks, making agent failures and spend anomalies easier to trace.

Factory’s deployed-engineering model treats agent adoption as workflow design: instrument the path from signal to deploy, build validators, and tie autonomy to measurable business outcomes.

MemLens scores individual agent memories, stores them by value, and exposes quality, latency, and token tradeoffs—an inspectable alternative to retaining every interaction equally.

Agents can turn production traces, evals, repository context, and observability skills into investigated issues or draft fixes. Human review remains more important as changes grow.

OpenAI debugged rare infrastructure crashes by analyzing core dumps at fleet scale, tracing them to a hardware fault plus an 18-year-old software bug. A useful pattern for hunting non-reproducible failures.

Your coding agent can now pull its own Agent Runs traces—reasoning, tool calls, token usage—from Vercel via MCP or CLI, so it can debug its runs and refine skills from real production behavior.

A deliberately simple online safety monitor — threshold an external verifier's signal, calibrate via risk control — matches sequential-hypothesis-testing monitors on math-reasoning and red-teaming datasets.