It isn't a FLOPS race. Decode is memory-bound, and the MI300X's 192 GB lets a model live on fewer GPUs than an 80 GB H100 can. The catch was never the silicon — it was ROCm. Here's where that tax stands in 2026.
Stop tool definitions and results from eating the context window: when to reach for dynamic tool search, when to reach for code execution, and why at scale you want both.
The cost of scaling a self-hosted model to zero isn't compute or container boot — it's the seconds-to-minutes of shoving tens of gigabytes of weights into empty GPU memory. That's the number that decides warm-vs-zero.
pass@k asks whether an agent can ever solve a task. pass^k asks whether it solves it every single time. For long-horizon agents those are different questions — and the gap is where production failures live.
When an orchestrator spawns twenty sub-agents that each retry on 429, the retries compound into a self-inflicted DDoS. The fix is upstream flow control, not smarter backoff.
Five AI-infra CVEs this spring were weaponized straight from the advisory text — no PoC, no patch window — because the serving layer ships a shell by default.
A model that solves a task 61% of the time can be reliable only 25% of the time. The gap between those two numbers is where production agents go to die.
Every shipping agent data breach has the same three ingredients. Once you see them, the fix stops being "make the model harder to fool" and becomes "remove one leg."
AWS's Strands lets the model plan its own path; LangGraph makes you draw the path first. The choice isn't graph versus no-graph — it's how much you trust the model to drive.
"Stateless" is a misnomer. The state never disappears — it relocates to the client and gets replayed, in full, on every single turn. The real question is who stores it and who pays to replay it.
Both Java AI frameworks hit 1.0 the same week and both now do RAG, tools, MCP, and observability. The real choice isn't features — it's where your app's center of gravity already sits.
Learned sparse retrieval promises dense-quality matching without giving up the inverted index. The catch isn't relevance — it's the query-time bill, and there's a mode that erases it.
For a normal service the threat is a static key leaked to a repo. For an agent the sharper threat is the agent itself being talked into reading its own environment and handing the key to an attacker.
The algorithm is the easy part. What actually gates agent RL in 2026 is building environments that emit a reward you can trust — here's how the open toolchain solves it.
Flat top-k retrieval returns the chunks most similar to your query. For "what is this document about?" that's exactly the wrong thing. RAPTOR retrieves at the right altitude instead.
Three benchmarks, three verification methods, three very different definitions of 'success' — so a single computer-use percentage tells you almost nothing without the asterisks.
They get filed as rivals because both promise "one API for every model." But one is a hosted marketplace you buy from, the other is infrastructure you run — and the smart move is often to use both.
Gemini's audio tokens look 10x cheaper than OpenAI's — until you learn it re-bills the whole conversation every turn. The real fork is transport, not price.
The real question isn't which isolation feature to use. It's where the tenant boundary lives — and what happens the one time a code path forgets to apply it.
Decoder-only LLMs took all the oxygen, but the model quietly doing your retrieval, reranking, and classification is still a small bidirectional encoder — and in late 2024 it finally got a 2024-era redesign.
One framework makes you draw the control-flow graph up front; the other lets it emerge from events. Pick by whether your hardest requirement is durable recovery or flexible composition.
Your engine computes a KV cache, uses it once, and throws it away. Offloading turns that scratchpad into a shared storage tier — and changes the question you should be asking.
Four open-weight MoE models now run real agents. The headline parameter counts are nearly decorative — pick by active params and post-training, not by the leaderboard screenshot.
The way you start an agent — schedule, HTTP event, or message queue — decides its retry, durability, and concurrency behavior more than the framework you write it in does.
A max-step counter is the reflex, and it's necessary — but it caps the damage without fixing the cause. Agents loop because the thing they see never changes, and that's a fixable problem.
Durable execution and checkpointing give you at-least-once replay, which is strictly worse for side-effecting tools — unless you attach a stable idempotency key before the call, not after the crash.
Loading every tool definition upfront doesn't just burn context — it tanks tool selection. The fix has three shapes: tool search, tool-RAG, and code execution. Pick by what you retrieve, and when.
Print statements debug code. But the agent's code did exactly what it was told — the bug is in the context the model saw and the decision it made there. You debug an agent by reading transcripts, not by stepping through functions.
Google's Antigravity, Cursor, and Claude Code now all hit ~80% on SWE-bench. So the real difference isn't who writes better code — it's where each one puts the work of checking it.
Google's Genkit is the framework that bundles the parts the others sell separately. The real choice isn't features — it's where your code runs and how much of your ops you want the framework to own.