AI agents for SRE sit at the intersection of on-call pain and demo hype. These posts separate what moved our incident response from what merely looked impressive in a slide deck.

Part of the series Building an Enterprise AI Agent Platform in Go.

Post What you’ll learn
AI-Augmented Incident Triage for SREs Practitioner take on what helps on-call vs demo theater
You Can’t Debug What You Can’t See — Observability for AI Agents Why traditional APM fails for agent workloads
Maintaining Tokenomics with Aiden Context budgets, compression, FinOps operating model

FAQ

What actually helps on-call SRE teams with AI agents?

Parallel context gathering with bounded tool loops, evidence from observability planes, and human-reviewable outputs — not open-ended autonomous remediation in the first iteration.

How do you observe AI agent workloads in production?

Traditional APM misses agent-specific failure modes. You need session-level traces, tool-call attribution, token budgets, and eval gates — not just request latency.

How do you control LLM costs for agent sessions?

Treat context as an operating budget: tiered memory, tool response compression, doom-loop detection, and per-session FinOps loops — cheaper models alone are not a strategy.