ai-platform
36 posts — newest first.
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Agent Sandboxes Start 6× Faster Now. Isolation Just Got Cheaper Than the Workaround.
Cloudflare cut median time-to-interactive from 4.0s to 648ms. The number matters because cold start is why teams share sandboxes they shouldn't share.
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NVIDIA Moved the Agent Watchdog to a Different Chip
The Open Agent Safety Platform puts enforcement on a DPU, outside the host the agent runs on. The instinct is decades old. The shape is the question.
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Agent browsing got 3–7× cheaper and 1.8× slower. Both numbers change your design.
Cloudflare's Kitesurf is a browser built for agents, not people. The measured trade-off, what it can't do, and why cheap browsing rewrites your threat model.
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A reasoning model just became a platform default. The lock-in isn't where you're looking.
Salesforce made Claude the default model across Agentforce and Slack. Where model dependency actually accrues, and the five exits worth keeping open.
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Your newest platform user is an agent. Build it a golden path.
Agents are becoming first-class platform citizens. That is an IDP problem: identity, quotas, golden paths, and a portal agents consume as an API.
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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.
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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.
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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.
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Your AI agent can't tell a quiet system from a broken collector
Autonomous remediation gates on model confidence and never on whether the telemetry is trustworthy. Telemetry integrity belongs in the gate too.
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Your top AI failure mode is saturation, and the SLI is in a header you throw away
Datadog found ~5% of production AI requests fail, nearly 60% on capacity limits. Provider saturation is an SRE problem you already have the primitives for.
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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.
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Compaction is a reliability event, and you are not measuring it
New research on long-horizon agents finds context compression degrades execution, not knowledge. Treat every compaction as a state transition and instrument it.
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Model end-of-life is a certificate expiry problem, and you already know how to solve it
Models retire on a schedule, the same model retires on different dates per platform, and 60 days' notice is a floor. Treat model EOL as inventory, not as news.
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One VM, one GPU, 100 customers: what it actually takes to sell access to an open-weight model
An open-weight model serving HTTP takes twenty minutes. Making it something 100 customers can share takes a control plane. The whole path, with numbers.
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Agent workflows belong in code, not just visual builders
OpenAI is winding down Agent Builder and platform Evals. Production agent workflows need code, tests, traces, and reviewable contracts.
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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.
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Round-robin is malpractice for LLM traffic: what the Inference Gateway actually fixes
The Gateway API Inference Extension is GA. Why a normal Kubernetes Service balances model servers badly, and how to tell if you need an InferencePool.
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GPU utilization is a lying metric: the unit economics of self-hosted inference
You can run a GPU at 100% utilization and waste most of it. The cost model that predicts your inference bill, and the five levers in payback order.
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Agents are deleting production and nobody is writing the postmortem
Your incident template assumes a deterministic system and a human decision-maker. Agent incidents have neither. Five sections that fix the template.
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Most of your AI platform's traffic doesn't need a frontier model
Small models got good enough in 2026. But the win isn't using them — it's making model choice a platform tier with a router, eval gate, and demotion path.
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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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The EU AI Act blinked — your logging requirements didn't
The EU AI Act omnibus moved high-risk deadlines to 2027–28. The logging, oversight, and inventory work is still yours — and reliability needs it anyway.
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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 Five Types of Agentic Memory (and When to Use Each)
Agentic memory is five things — working, episodic, semantic, procedural, entity — each with its own storage, eviction, and failure mode. A decision guide.
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Agentic AI Patterns: The Decision Guide (Part 1 of 3)
Six agentic AI patterns — ReAct, Plan-and-Execute, Critic, fan-out, HITL gate, Supervisor — with a decision flowchart for picking one before you build.
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Agentic AI Patterns: The Maturity Model (Part 3 of 3)
A five-level agentic AI maturity model, from manual to multi-agent mesh — with a self-assessment and where regulated industries should draw the line.
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Agentic AI Patterns: Where They Break in Production (Part 2 of 3)
Every agentic AI pattern looks clean in a demo. Where each one breaks in production, the signals you're hitting them, and mitigations that actually work.
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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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The AI-native SRE stack — a 2026 reference guide
A practitioner's map of the AI-native SRE stack in 2026: six layers from telemetry to bounded remediation, and an honest read on where AI pays off.
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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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Observability for AI systems — what changes when your service calls an LLM
Golden signals miss the failure that pages you: a confident, well-formed, wrong answer. What AI observability adds — context as a span, quality as a signal.
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The CAP theorem in AI-native distributed systems
CAP didn't get repealed when LLMs showed up. How the C/A/P trade-offs shift when the datastore is a vector index, context graph, or retrieval layer.
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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.