Filesystem vs Vector Database for Agent Memory: Why 2026 Agents Write to Files
The year's quietest architecture shift is agents moving their memory out of vector stores and into plain files. It isn't that memory got better — it's that teams stopped using a retrieval tool for a state problem.
5 min
Claude Agent SDK Billing: Why the June 15 Subscription Credit Split Was Paused
Anthropic tried to give programmatic Claude usage its own bill, then reversed it on the day it was due. The retreat doesn't fix the problem it exposed.
4 min
How to Evaluate a Deep Research Agent: BrowseComp vs DeepResearch Bench
The benchmarks for web-browsing agents split along a fault line the coding benchmarks never had — and the trick that makes one of them work quietly hides which half of your agent is actually good.
5 min
The Agent Control Specification (ACS): A Portable Control Plane for AI Agents
MCP standardized how agents connect and A2A standardized how they talk. The Agent Control Specification standardizes the part that decides whether you can deploy — what an agent is allowed to do — and its smartest move is what it refuses to standardize.
5 min
A2A vs ACP vs AGNTCY: The Agent Interoperability Protocols, Compared
The query assumes three live standards fighting for the agent-to-agent layer. Two of the three answers are already settled — and the third isn't even in the same race.
5 min
WASM vs MicroVMs vs V8 Isolates: Sandboxing AI-Generated Code
The choice isn't speed versus security. It's whether the model is writing code that orchestrates your tools or code that needs the whole operating system — and that picks the security model for you.
5 min
Terminal-Bench vs SWE-bench: Why Patching Code and Operating a Shell Are Different Skills
SWE-bench hands an agent a broken test and a healthy repo. Terminal-Bench hands it a live machine and lets it break things. That's why a top SWE-bench score tells you almost nothing about the second number.
5 min
RULER vs Needle-in-a-Haystack: How to Measure an LLM's Real Context Length
The number on the spec sheet is a memory allocation, not a comprehension score. A needle test passing at 1M tokens tells you the model can find a string — not that it can use the context. Here's the benchmark that measures the difference.
5 min
Recovery-Bench: Why Top Agents Still Fail to Recover From Their Own Mistakes
A new benchmark replays an agent's failures into a corrupted environment and asks a fresh model to fix them. The leaderboard reorders — recovery is not the same skill as solving.
4 min
Record and Replay Testing for AI Agents: Deterministic Tests Without Live LLM Calls
You can freeze an agent run and play it back in CI — but there are two layers you can record at, and picking the wrong one means your tests stop catching the bug you actually care about.
5 min
Prompt Injection Defense: Detection Guardrails vs Defending Agents by Design
A classifier that blocks 98% of injections sounds like a fix. Against an attacker who can retry, a nonzero bypass rate isn't a wall — it's a toll. The defenses with real guarantees don't detect the bad instruction at all; they cap what any instruction is allowed to cause.
5 min
Prompt Format: JSON vs XML vs Markdown vs YAML — and Why Input and Output Want Opposite Things
The reflex is to wrap everything in JSON because it's 'structured.' On the way into a prompt that's a token tax; on the way out it's an accuracy tax. The right answer is split, not single.
4 min
Prompt Caching Pricing in 2026: Anthropic vs OpenAI vs Gemini vs Bedrock
Every provider now sells the same ~90% discount on repeated context. The number on the brochure is not where the bills actually diverge — three quieter terms are.
4 min
The OWASP MCP Top 10, Explained: A Security Checklist for Tool-Connected Agents
OWASP now has a third Top 10 — one scoped to a single protocol. The surprise isn't a new class of AI attack; it's that connecting an agent to MCP servers re-exposes 2010-era web and supply-chain bugs through a channel that auto-executes them.
7 min
NVIDIA NIM vs vLLM vs TGI: How to Self-Host LLM Inference in 2026
One of these isn't an inference engine at all — it's a wrapper around the other two. Sorting that out is the whole decision, and it just got simpler because one contender quietly left the race.
4 min
MTEB vs MMTEB vs RTEB: How to Read an Embedding Leaderboard in 2026
The number at the top of the MTEB leaderboard has quietly stopped meaning what you think it means. Here is which board to read, and why the newest one hides half its test set on purpose.
4 min
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