The decision pages — every “X vs Y” head-to-head and “best X for Y” guide for building AI agents, grouped by what you're choosing between. 29 and counting.
Million-token windows were supposed to kill retrieval. The benchmarks say something stranger — the choice is really between two different failure modes, and only one of them is loud.
All three clear the recall-and-latency bar for almost any agent you'll build. The real decision is where the operational cost lives — and there's a query volume where the answer flips.
A reranker is the cheapest large win left in a RAG pipeline — a stateless model you bolt on after retrieval. The trap is choosing one by leaderboard rank instead of the two things that actually decide it.
The chunk-size A/B test is the most over-run experiment in RAG. The teams winning on retrieval stopped tuning how they split and started fixing what each chunk forgets.
Microsoft GraphRAG, LightRAG, and LazyGraphRAG all promise smarter retrieval. The honest question isn't which to pick — it's whether your queries are the kind a graph can even help.
The benchmarks everyone argues about measure the thing that almost never decides the choice. The real axis is where your vectors live — and whether you can afford to keep them there.
Voyage, OpenAI, Gemini, Cohere, and open-weight BGE all top some leaderboard. The MTEB score you're comparing is the least important number in the decision.
The second wave of agent frameworks is leaner, typed, and vendor-backed — and underneath the branding, they're quietly converging on the same idea.
One hands you Anthropic's production agent loop already wired up; the other hands you a blank graph and a state machine. The choice is less "which framework" than "how much of the loop do you want to own."
They started on opposite ends — one indexed your documents, one chained your calls. In 2026 they've converged. The real choice is which abstraction you want to debug at 3am.
All three claim to build multi-agent systems. The real question isn't features — it's who owns the control flow, and the answer changes which one is the right call.
Three projects give an agent a browser, but they disagree on what a page even is — pixels, DOM, or accessibility tree — and that one choice sets your token bill.
They all give an agent the web, but they hand it back at different stages of doneness — raw links, cleaned pages, semantic matches, or a finished sourced answer. The price tracks exactly how much reading they did for you.
All three turn a webpage into clean markdown an LLM can read. They are not competing on that — they sit on three different rungs, and picking by star count gets the rung wrong.
They are not competing ways to give a model tools. One is the engine; the other is a distribution standard wrapped around it — and you pay for the wrapper in tokens and attack surface.
Stop reading "A2A vs MCP" as a fork in the road. One protocol points your agent down at tools; the other points it sideways at other agents. Here is how to use both without picking a loser.
Three popular eval frameworks that look interchangeable answer three different questions — pick the one that matches the question you actually have.
The real choice isn't which dashboard looks nicer — it's what unit of work you trace and who owns the trace data after the agent finishes.
Every agent ends up talking to more than one model provider. The library you put in the middle decides whether that seam stays a proxy or quietly becomes your control plane.
The benchmark everyone argues over is the wrong one. The engine you should run is decided by how much context your requests share — not by whose tokens-per-second screenshot is biggest.
Three "agent sandboxes," three different machines underneath. Choose by your latency-and-lifetime profile and your isolation primitive, not by the feature grid.
Every agent that runs longer than a single request eventually crashes mid-thought. The engine you pick to survive that crash decides how you're allowed to write the loop.
The fight you think you're having — open pipeline vs hosted LLM parser — ended last year. A 1.2B model on your own GPU now wins the part that actually matters.
The open-versus-closed debate in agents is framed as a fight over frameworks — but the real leverage moved to a layer where the distinction barely applies.