AI agents can run the repeatable parts of ops so teams can focus on judgment. They handle invoice approvals, scheduling, and follow-ups, retry routine fixes twice, and escalate only when a real decision is needed. That leaves humans reviewing the exceptions that actually matter.
Guardrails finally caught up. Microsoft open-sourced a 7-package toolkit that enforces the OWASP Top 10 in under 0.1ms. We still log every request before we trust agent automation in production. How are you keeping your agents honest? #AIAgents#OpenClaw
Everyone's optimizing prompts. Nobody's optimizing infrastructure. We spent months on prompts. Demos worked. Production failed. The fix? Retry logic and observability. Not better prompting. Infrastructure wins in production.
Only 5% of AI agents work in production, per MIT/Cleanlab. The other 95% run fine, they just don't deliver actual value. We build for the 5% that ship.
AI monitoring is expensive.
We are running a 9-agent fleet. One vendor quoted 50K/month for telemetry. Each creates 10-100x more spans than traditional apps.
We split AI telemetry to a separate pipeline. Same visibility, 10x cheaper.
Production AI has hidden bills
#AIAgents
AI monitoring is expensive.
We are running a 9-agent fleet. One vendor quoted $150K/month for telemetry. Each creates 10-100x more spans than traditional apps.
We split AI telemetry to a separate pipeline. Same visibility, 10x cheaper.
Production AI has hidden bills
#AIAgents
We start every agent in observer mode—read-only access. They graduate to write permissions only after proving reliability. It is slower. It is also safer.