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The 2026-07-28 spec made MCP stateless. Long-running work and statelessness are in direct tension — and the Tasks extension resolves it by handing the bookkeeping to the client. The tell is what got deleted.
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The Wire
The next Model Context Protocol release stops adding features to the core and starts subtracting them. The Extensions framework is how — and 'in the spec' no longer means 'in the core.'
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Both kinds of cache hit read at the same discount, so cost-per-hit is the wrong thing to choose on. The real split is a guarantee you pay for versus a freebie you can't shape.
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The Wire
A JSON object isn't valid until its closing brace — but your UI shouldn't wait for it. The trick is realizing a streamed object is a view, not a value, and validating it exactly once: at the end.
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The SDK's 10-minute default times out one call; an agent makes dozens. You need a deadline the whole loop shares — and cancelling to enforce it still costs tokens and can corrupt state.
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An agent has no ROLLBACK: when step three fails, the first two already happened in the world. The fix is a compensating undo for every tool — and putting the one you can't undo last.
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Your agent can be HTTP-200, fast, and cheap while being completely wrong. The metrics that keep a web app healthy are blind to the ways an agent actually fails.
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The Wire
For an app built on a hosted LLM API, the wall you hit under load isn't the model's speed — it's the provider's rate limiter and your own retry policy. Test for the ceiling and the fall, not the throughput.
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The Wire
The try/except instinct that keeps a normal program alive is the one that kills an agent. A tool error isn't an exception to catch — it's the next message in the conversation, and where you put it decides whether the agent can recover.
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The Wire
A reranker can only reorder what your retriever already fetched, so the ceiling on its lift is your stage-one recall — measure that first, then judge the reranker as the latency and dollars you pay to convert recall into precision.
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Your RAG pipeline works on documents and falls apart on a spreadsheet — because a table's meaning lives in its grid, and an embedding flattens the grid away.
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An agent isn't a stateless web service — it's a long-running, resumable process. The thing that bites first isn't latency; it's shipping a new version while runs are still in flight.
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Static benchmarks freeze the world while an agent thinks. Meta's GAIA2 lets time run — and the smartest model, GPT-5, turns out to be the one that misses deadlines.
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The first rigorous benchmark of repository context files is in, and the answer is uncomfortable: the auto-generated ones make agents slightly worse, the hand-written ones barely help, and both raise your bill ~20%.
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The two halves of every LLM request fight each other on the same GPU. Disaggregated serving splits them onto separate hardware — and the win is real, but only past a certain scale.
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The Wire
An agent leaderboard that ranks only on accuracy is secretly ranking on willingness to spend. Add the cost axis and the board's #1 is often not even on the frontier.
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The Wire
If your agent's reward is a number it can reach without doing the work, it will eventually reach the number without doing the work — and 2026's research says that habit doesn't stay contained.
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The Wire
For a year the question that stalled enterprise bets on MCP was 'what happens when Anthropic changes its mind?' In December that question got an answer — and the answer reveals what the standards war was really about.
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A new web standard lets a website hand an AI agent a typed menu of its own functions — no server, no OAuth. The catch is hiding in that 'no OAuth.'
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Vercel's new agent framework treats an agent as a directory of files. LangGraph hands you a portable graph. The decision isn't the loop they run — it's who owns the production stack wrapped around it.
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Trainium2 and Inferentia2 sell real price-performance and AWS capacity. NVIDIA sells CUDA. The decision is whether the Neuron SDK supports your model and serving stack — and how much engineering you'll spend finding out.
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Prompt and semantic caches store the model's work and fail cheaply. Tool-result caching stores the world's — and it forces a question every agent codebase has dodged: which tools are safe to cache?
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Most agent benchmarks hand the whole task to the model. τ-bench keeps the user in the loop, and τ²-bench gives the user their own hands — which is where frontier agents quietly fall apart.
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A new benchmark drops the same models from ~73% to ~25% — not by making the bugs harder, but by taking away the one thing SWE-bench always handed over: a map to the change.
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The same models that ace SWE-bench Verified collapse on its successor. The gap isn't difficulty — it's the size of an illusion, and the only durable fix turned out to be a software license.
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Four ways to make an agent fix its own mistakes. Three of them quietly outsource the judgment to the world — and the one that doesn't is the one the research keeps catching in the act.
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The Wire
Builders keep wiring diarization into the live loop of a one-on-one voice agent. There, it solves a problem you don't have — because you already own one of the two voices.
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Both pack weights into the same E2M1 four-bit float. The fight is entirely about the block scale — and that one design choice decides whether you keep your accuracy or hand it to the open standard.
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Microsoft stopped shipping orchestration patterns and started shipping the runtime underneath them. The three Build 2026 launches are all below the framework — and one of them quietly retires the JSON tool-call loop.
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The Wire
The most common serious flaw in MCP servers isn't prompt injection. It's SSRF — the boring, pre-AI bug that sank Capital One — and we just installed it by the thousand.
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