Agents
22 posts — newest first.
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MCP 2026-07-28 shipped: the stateless core is the headline, header routing is the story
The final MCP spec deletes sessions, mirrors requests into HTTP headers, and turns elicitation into multi-round-trip calls. What breaks, and what to audit.
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Your runbooks are lying to your agents: RAG for on-call knowledge
On-call agents are only as good as what they retrieve. Making runbook RAG fresh, exact-match aware, and poison-resistant — with a runnable starter repo.
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MCP is the hands, A2A is the handshake: the two-layer agent protocol stack
A2A hit v1.0 with 150+ orgs behind it. MCP connects agents to tools; A2A connects agents to each other. What the two-layer stack means for platform teams.
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Every observability vendor now sells an AI SRE agent. Here's how to evaluate one.
Datadog, Dynatrace, New Relic, AWS — every incumbent now ships an AI SRE agent. A field guide for evaluating one before it touches production.
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The OWASP Agentic Top 10, translated for platform teams
OWASP's Agentic Top 10 reads like a security doc, but the mitigations are platform controls. Mapping all ten risks to infrastructure you already run.
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Your agents need identities, not API keys
Every AI agent is a non-human identity — most run on shared, long-lived API keys no IAM review sees. Per-agent identity and your credential blast radius.
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Token FinOps: the third budget your agents are spending
Error budgets, context budgets — agents add a third: dollars. Agent tasks burn 5–30× chatbot tokens, and cost-per-token is the wrong metric.
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Tracing the agent loop: OpenTelemetry's GenAI conventions, read like an SRE
Your agent is a distributed system wearing a chat interface. OpenTelemetry's GenAI conventions make it debuggable — what v1.41 covers and what's moving.
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Frontier models still fail half your incidents: reading ITBench-AA like an SRE
ITBench-AA put frontier models against 59 real Kubernetes incident diagnoses — all scored below 50%. What the benchmark measures and how to use it.
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Autonomy is a budget, not a toggle: error budgets for AI operators
SRE solved runaway release risk with error budgets. The same mechanism governs AI agents: authority per action class, demoted fast, promoted slow.
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The reliability gap: a framework for trusting autonomous SRE agents
An autonomous airline agent rebooked 1,247 passengers wrong in one weather event. Trusting agents is a reliability problem — here's how to measure it.
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Chaos engineering for MCP: break your tool-call plane before production does
LLM calls fail 1–5% of the time and agent tasks fan out into 10–20 tool calls. How to fault-inject your MCP layer with mcp-chaos before production does.
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The trust gap: bounded autonomy for AI SRE agents
SREs face 50+ alerts a day at 60% false positives while vendors promise autonomous resolution. The autonomy ladder: what an AI agent should never do alone.
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MCP goes stateless — what the 2026 release candidate means for your SRE tooling
The 2026-07-28 MCP release candidate deletes the session handshake for a stateless HTTP core and hardens OAuth. What changes for your agents, and when.
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Context engineering: the window is a budget, not a bucket
The context window is your agent's working memory, not a junk drawer. Four operations — write, select, compress, isolate — and a token budget you allocate.
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Agent sprawl is your next production incident
Teams shipping AI agents are recreating 2015's microservices sprawl with worse observability. The governance surface that contains it before it pages you.
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No anonymous inference endpoints — the MCP security principle you're probably violating
The NSA and NIST put MCP on notice: agents are a funnel for prompt injection and privilege abuse. Why 'no anonymous inference endpoints' — and how to comply.
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Harness engineering: the third phase of AI maturity
Agent = Model + Harness, and in 2026 the harness is the bottleneck. What a production-grade SRE harness contains, with a ~40-line reference implementation.
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What is function calling (tool use)?
A primer on function calling — the JSON-schema contract that lets an LLM invoke your code. The request/response loop, parallel calls, and forced tools.
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What is an AI agent? A primer for cloud engineers
A primer on AI agents — the perceive-reason-act loop, what separates an agent from a one-shot LLM call, and the classical agent types SREs now operate.
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What is Model Context Protocol (MCP)?
A primer on Model Context Protocol — the open standard that lets AI applications talk to tools through one interface. Hosts, clients, servers, transports.
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The MCP gateway pattern: five jobs your agent runtime can't skip
Letting agents call MCP servers directly repeats the no-API-gateway mistake. The five jobs an MCP gateway must do, with reproducible patterns for each.