Round-robin is the wrong way to route an LLM request. Kubernetes now has a GA'd standard that lets the gateway pick a model server by live KV-cache pressure and queue depth instead — and it changes what a load balancer is.
With ADK 2.0's GA, LangGraph, OpenAI's Agents SDK, Google's ADK, and Microsoft's Agent Framework all now run on a graph execution engine. The programming model war is over. It settled the easy question.
Microsoft and Google both now let you define an agent in YAML instead of code. The split isn't about simplicity — it's about whether your agent's logic lives in its wiring or in its decisions.
The Kubernetes-native serving stack got very good at spreading a model across a cluster. But in 2026 your GPUs aren't in one cluster — they're scattered across clouds by price and availability, and that's a different problem.
The summary your long-running agent writes to stay under its token budget is lossy in one direction: it keeps the rules that fire and drops the rules that forbid. New research puts a number on how fast safety erodes.
Approximate nearest-neighbor search is a tax you pay to survive scale you may not have. Below a few hundred thousand vectors, exact brute-force is faster, perfectly accurate, and has no index to rot.
Amazon Q auto-ran an MCP config out of any repo you opened, with your live AWS keys in the process. It got a CVE. The identical bug in Claude Code, Cursor, Gemini CLI and Copilot got declared working-as-designed — because the trust prompt you inherited from your editor was never a consent to run code.
Nearly a year after the first Comet and Atlas exploits, the browsers' own makers say prompt injection may never be fully solved. The reason is structural, not a bug waiting for a patch.
An autonomous agent found 21 genuine zero-days in FFmpeg for about $1,000. The same technology just made curl kill its bug bounty. Discovery got cheap; disposition didn't.
A June 2026 paper clocks three popular memory frameworks on the same benchmark: 118K, 632K, and 3.26M tokens per query. The 500x spread isn't noise — it's a design choice most teams never realize they're making.
For 25 years the web tried to detect bots by behavior and kept losing. Web Bot Auth gives up on detection and asks the bot to sign its name instead — and the big agent makers have already started doing it.
Both shipped the same six production features in 2026. The choice isn't capabilities — it's which half of your agent you're willing to lock to a vendor.
A Chinese lab shipped a 266B/10B-active model that claims to decompose and finish 100+ step tasks on its own. The benchmark line isn't the story — the category claim is.
Interruptible GPUs scare people because of training horror stories. For stateless inference the math inverts — there's nothing to checkpoint, so the only real tax is cold start.
Both drive a real browser from natural language. But one reads the DOM and one looks at pixels — and that single perception choice decides your cost per step, your reliability on ugly sites, and whether you can even ship it in a closed product.
Swapping LLM providers in one line is true for a chatbot and a lie for an agent. The cage is one layer up, in tool-calling behavior — and no gateway unlocks it for you.
OpenAI and Google ship deep-research as a closed feature. These seven open repositories let you run the same plan-search-read-synthesize loop on your own models, your own sources, and — if you want — entirely on your own machine.
Most MCP servers are REST APIs underneath. The honest question isn't which transport to use — it's how much of your API to expose, and the data says the answer is about a fifth of it.
The biggest Model Context Protocol revision since launch deletes the session, the handshake, and even the client-side LLM call. The headline isn't new features — it's that the protocol got smaller.
The benchmarks that grade an agent's memory just moved the finish line from 9,000 tokens to 10 million — and the new one proves a million-token context window doesn't buy you long-term memory.
An LLM judge flips up to a third of its verdicts when you swap the answer order, and scores its own writing 10–25% higher. Three biases corrupt your evals — and only one has a cheap fix.
After a year of churn that made it a punchline, LangChain shipped a 1.0 whose headline feature is the thing frameworks never promise: that it will stop moving under you.
All three move messages between agents. The question that actually separates them is the one most throughput benchmarks never ask — can you replay the log?
They get used as synonyms, and that confusion is why teams 'add a guardrail' and stay wide open. A jailbreak attacks the model's policy; prompt injection attacks your application's trust boundary.
A chatbot's system prompt sets a personality. An agent's is control logic the model rereads on every turn of the loop. Stop writing a persona and write a policy.
You can't A/B test an agent the way you A/B test a button. The unit of variance is a trajectory, not a click — so the gate has to be offline, and "shadow mode" means something different than it does for a model.
Mem0 says 92.5% on LoCoMo. Mastra says 95% on LongMemEval. Zep corrected its own 84% to 58%. They can't all be right — and the baseline that beats them all is the one no vendor charts.
The MTEB leaderboard is a prior, not an oracle. The model that wins your RAG system is the one you measure on a few hundred of your own labeled queries — here is how to build that eval.
A deep research agent hands you a long, confident, well-structured report. Grading it means measuring two different things at once — how good it reads, and whether a single sentence is actually supported.