MCP
21 posts — newest first.
-
Prompt Injection: Why Your Agent Believes the Wrong Text
An LLM sees one stream of tokens, not instructions and data. That is why prompt injection has no parser fix — and why provenance, not filtering, is the control.
-
The Model Hardware Standard is the first agent spec where rollback isn't an option
Anthropic's MHS gives agents read/write control of lab and factory hardware. What it guards, what it doesn't, and the controls you have to build yourself.
-
OWASP published an MCP Top 10. Go count your servers before you read it.
OWASP now has a Top 10 dedicated to Model Context Protocol. The list is useful — but the census behind it is the part that should worry your platform team.
-
MCP has a standards body now. That changes your procurement, not your code.
MCP now lives under the Agentic AI Foundation at the Linux Foundation. What vendor-neutral governance actually buys a platform team.
-
MCP's next release deletes your polling loop. Start building for it now.
The MCP roadmap published 22 August 2026 names five priorities. Three change how you operate agent infrastructure — and you can prepare for all three today.
-
Your agent installs Markdown from the internet and runs it. We spent 20 years learning not to do that.
Agent skills are dependencies with none of the controls. What the 2026 research found, why scanners miss it, and seven fixes for platform teams.
-
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.
-
AI SRE is officially a category. Buy it like an SRE, not like a CIO.
Gartner's first Market Guide for AI SRE tooling projects 85% adoption by 2029. Six evaluation questions for cutting through the category hype.
-
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.
-
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.
-
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.
-
Agentic Resource Discovery: I Read the Spec, Then Published a Catalog
Google, Microsoft, and Hugging Face shipped Agentic Resource Discovery. The real ai-catalog.json schema, a working catalog, and the gotchas that break it.
-
OKF: The Missing Context Layer for AI Agents
The Open Knowledge Format gives agents a structured vocabulary for what data they're touching and where it came from — auditable, not blind, reasoning.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.