MCP Goes Stateless: What the 2026 Spec Changes for Agent Builders
The 2026-07-28 release candidate kills the session and the handshake, graduates Tasks and Apps to extensions, and deprecates Sampling. The real story isn't statelessness — it's a shrinking core.
5 min
How Many GPUs to Serve an LLM: Capacity Planning Is a Memory Problem, Not a FLOPs One
Decode is memory-bandwidth bound, so a GPU's TFLOPs barely predict serving capacity. What caps concurrency is the KV cache. Here's the actual arithmetic, with a worked example.
5 min
LangGraph vs Microsoft Agent Framework: Who Owns the Run Loop in 2026
They ship the same orchestration patterns now, so stop comparing them on patterns. The real fork is where your production agent actually runs — in code you hold, or in a cloud you rent.
4 min
How to Track LLM Costs Per Customer in a Multi-Tenant App
The provider's per-user field won't give you an invoice, and raw token counts lie. The honest unit of attribution is the priced token — after caching, batching, and hidden thinking.
4 min
How to Test an AI Agent With Simulated Users (and Why the Fake User Is the Hard Part)
You can't script a conversation, so you hand the user's seat to a second LLM. That move doesn't solve your measurement problem — it relocates it into a simulator you never validated, and the default one grades on easy mode.
5 min
How to Roll Out a New LLM in Production: Shadow vs Canary vs A/B Testing
The progressive-delivery playbook assumes a bad release trips an alarm. A worse model returns HTTP 200 on time with a fluent wrong answer — so the canary you copied from your web service is blind to the only failure that matters.
6 min
How to Price an AI Agent: Seat vs Usage vs Outcome
Every pricing model for an AI agent is really a decision about who absorbs the inference bill — and the floor under any outcome price is the cost of producing that outcome.
5 min
How to Keep a Vector Database in Sync With Your Source Data
Adding and updating vectors is the easy half — upsert overwrites by ID. The half everyone forgets is deleting the orphans, because a stale vector never errors. It just keeps getting retrieved.
4 min
How to Evaluate AI Agent Memory: LoCoMo, LongMemEval, and Why Long Context Isn't Enough
Bigger context windows don't fix forgetting. The benchmarks that actually test agent memory — LoCoMo and LongMemEval — and what their question categories reveal about where it breaks.
4 min
How to Evaluate a Voice Agent: Why Text-Agent Metrics Miss the Real Failures
Transcription accuracy is table stakes. The failure surface that actually loses calls is conversational timing — turn-taking, barge-in, and an end-to-end latency budget you have to measure component by component.
6 min
How to Benchmark LLM Inference: Why One Tokens-Per-Second Number Is Lying to You
A single throughput figure is uninterpretable without the load that produced it and the prompt shape you fed in. The honest output of an LLM benchmark is a curve, and the number that matters is goodput — the most traffic you can serve while still meeting your latency SLO.
5 min
How to Add LLM Evals to CI/CD Without Building a Flaky Gate
You wire your eval into GitHub Actions, gate the merge on it, and a week later it's red on a PR that changed nothing. The fix isn't a retry — it's admitting an eval is a measurement, not an assertion.
5 min
FlashAttention vs PagedAttention: Two Different Bottlenecks, Not Two Choices
One speeds up the attention math; the other stops your KV cache from wasting most of the GPU. You run both — and the friction where they meet is the actual story.
5 min
Expert Parallelism: How Giant MoE Models Are Actually Served
A trillion-parameter MoE only fires a fraction of itself per token. Expert parallelism scatters those experts across dozens of GPUs — but the hard part was never the split. It's the all-to-all traffic and the hot experts, and they only pay off when you're drowning in load.
5 min
Elasticsearch vs OpenSearch vs Vespa: Choosing a Hybrid Search Engine for RAG
Two of these are near-twins separated by a license; the third is a different kind of machine entirely. The hard part is realizing you're answering two questions, not one.
4 min
A Circuit Breaker for LLM API Calls — and Why It Has to Trip on Cost, Not Just Errors
The textbook breaker opens when calls start failing. The incident that actually bankrupts an agent is a loop where every call succeeds — so you need a second breaker that watches money, not errors.
4 min
AMD MI300X vs NVIDIA H100 for LLM Inference: The Memory Wall and the Software Tax
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.
5 min
Too Many Tools: Tool Search vs Code Execution for Agents at Scale
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.
6 min
Scale to Zero for LLM Inference: Why Cold Starts Are a Weight-Loading Problem
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.
5 min
Pass@k vs Pass^k: Measuring Whether an Agent Is Reliable, Not Just Capable
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.
5 min
Backpressure for AI Agents: Why Exponential Backoff Makes Fan-Out Worse
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.
5 min
Self-Hosted AI Tools Are Now Exploited in Hours: Inside 2026's Advisory-to-Attack Window
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.
5 min
Why Multi-Step AI Agents Fail in Production (and How to Make Them Reliable)
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.
5 min
The Lethal Trifecta: How AI Agents Get Tricked Into Leaking Your Data
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."
5 min
Strands Agents vs LangGraph: Who Drives the Agent Loop
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.
4 min
Stateful vs Stateless AI Agents: Where the State Actually Lives
"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.
4 min
Spring AI vs LangChain4j: Which Java Framework for Your LLM App?
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.
4 min
SPLADE vs BM25 vs Dense: Does Learned Sparse Retrieval Beat Hybrid Search?
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.
5 min
Secrets Management for AI Agents: Why the Model Should Never See the Key
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.
5 min
Reinforcement Learning for AI Agents: RLVR, Verifiable Rewards, and the Environment Problem
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.
5 min