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They are not two answers to one question. RAG fixes what the model doesn't know; fine-tuning fixes what it won't do the way you need. Pick by the failure, not the fashion.
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There are two things called FastMCP, and one of them lives inside the official SDK. Picking the right way to build an MCP server starts with untangling that — and deciding how much you want the framework to do for you.
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Three "agent sandboxes," three different machines underneath. Choose by your latency-and-lifetime profile and your isolation primitive, not by the feature grid.
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Three popular eval frameworks that look interchangeable answer three different questions — pick the one that matches the question you actually have.
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They all surface when you Google "AI chat UI for agents," but they own three different layers — and the ones worth shipping often stack rather than swap.
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Most RAG retrieval failures are context lost at chunk boundaries — contextual retrieval fixes them at index time, cheaper than a bigger embedding model or GraphRAG.
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Prompt engineering optimized a string. Context engineering manages a finite, decaying budget — because the context window is not a bucket you fill, it is attention that rots as it fills.
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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."
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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.
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The model that emits a correctly-shaped tool call once is rarely the one that holds up across a multi-turn conversation and eight repeated trials. Pick by failure mode, not top-line score.
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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.
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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.
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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.
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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.
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Caching LLM calls by meaning can cut your bill and your latency — or it can confidently serve last user's answer to this user's question. The whole game is the similarity threshold nobody tunes.
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Per-prompt model routing promises GPT-quality answers at a fraction of the bill. The honest 2026 answer is that it's a cost lever with a threshold, not a free one — and a neutral benchmark disagrees with the marketing.
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Every major provider will sell you a 50–90% discount on repeated context. The catch is a single rule that quietly fights how agents are built.
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All three give you a drag-and-drop canvas for building AI agents. The choice that actually matters is hidden underneath: what each one thinks it's automating, and whether its license lets you ship it.
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Using a model to grade your model feels like measurement. Until you learn what the judge is actually rewarding — verbosity, position, and its own prose — it's closer to a focus group of one.
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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.
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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.
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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.
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Three Python libraries that treat your prompt as a parameter to be tuned, not a string to be hand-crafted. They disagree about what the optimizer needs from you — and that's the whole decision.
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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.
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Whisper tops the accuracy leaderboard and loses the conversation. For a live voice agent, the number that decides whether the bot feels human isn't word error rate — it's who detects the end of your turn.
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The old way to choose was "which one scales." That axis has quietly collapsed — all three now run on a laptop and across a cluster. What's left is a question about default posture and the ops bill you're signing up for.
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Agents that write their own code forced an old infrastructure question back into the open — where, exactly, does the security boundary live, and what does it cost to drop it a layer lower?
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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.
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The memory libraries aren't competing on accuracy. They're competing on geography — where the remembering happens relative to your agent's loop. Pick the place, not the benchmark.
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Agent observability didn't invent a standard. It surrendered to a boring one from 2019 — and in doing so quietly retired the log as the unit of truth.
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