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Deep-dive guides on AI agents, agent orchestration, MCP, and developer tooling.
28 posts found
Prompt Versioning and Regression Testing: Why Prompts Need a CI Pipeline Too
A one-line system prompt tweak that 'fixes' one failure case can silently break three others. Here's how teams are starting to treat prompts as versioned, tested artifacts instead of strings in a config file.
Context Engineering: What Changed When Prompt Engineering Outgrew the Prompt
Prompt engineering asked what to say to the model. Context engineering asks what the model actually needs to see, in what order, and what gets thrown away to make room for it: the discipline agents forced into existence.
Claude Haiku 4.5 vs. GPT-5.4 Mini vs. Gemini 3.8 Flash: The Economics of Cheap Inference
The three labs' cheap-tier models now sit within a quarter-dollar of each other per million input tokens, so the real differences are in release cadence, cache economics, and how much of your "cheap" budget goes to invisible reasoning tokens.
How Background Coding Agents Actually Work: Sandboxes, Diffs, and the PR Review Bottleneck
Codex, Copilot's cloud agent, Cursor's cloud agents, Jules, Devin, and cloud Claude Code sessions all follow the same five-stage pipeline. The differences that matter are in sandbox scope, repo access, and where the real trust boundary sits.
MCP Sampling: How a Server Asks the Client's Model to Think for It
Sampling is the MCP primitive that inverts the relationship, letting a server without its own model borrow the client's LLM through a two-gate approval flow. Here is how it works, and why the 2026-07-28 spec revision deprecated it.
Letta, Mem0, and LangGraph Stores: Three Different Answers to Where Agent Memory Lives
Letta, Mem0, and LangGraph checkpointers all get called "agent memory," but they put the decision to remember or forget in three different places: an agent runtime, an external service, or your own application code.
LLM-as-Judge Evaluations Have Five Known Biases: Here's How to Design Around Them
Position bias, verbosity bias, and self-preference bias can silently distort agent eval scores. Here is how to design a judge pipeline that resists them, plus why trajectory evaluation catches what outcome scoring misses.
What SWE-bench Actually Measures vs. What the Leaderboard Tells You
SWE-bench is the most credible agentic coding benchmark available, but its leaderboard number answers a narrower question than most headlines imply. Here is exactly what it does and does not test.
Where AI Actually Works in Scientific Research: Protein Design, Materials Discovery, and the GNoME Lesson
AlphaFold's Nobel Prize and GNoME's 2.2 million materials both got real headlines, but the honest story is about candidate generation, not discovery, and the gap between those two claims is where the hype lives.
Claude Sonnet 5 vs. GPT-5.6 vs. Gemini 3.1 Pro: What the Pricing Table Doesn't Tell You
A side-by-side look at pricing, context windows, and tiering across the three frontier model families as of August 2026, and why the spec sheet undersells how differently they actually behave in agent loops.
Prompt Engineering for Agents Is a Different Discipline Than Prompt Engineering for Chat
In agent systems, instructions live across four surfaces (system prompt, tool schemas, tool results, and few-shot text), not one. Most prompt debugging still only looks at the first.
Naive RAG, Agentic RAG, and GraphRAG: What Actually Changes Architecturally
RAG is not one architecture; it is three structurally different systems with different costs and failure modes. Here is what actually changes between naive, agentic, and graph-based retrieval, and how to pick without over-building.
Why Your Agent Benchmark Score Doesn't Predict Production Reliability
Benchmark leaderboards measure task completion under lab conditions, not the compounding step failures that sink agents in production. Here is the math, and a blueprint for an eval harness that actually predicts reliability.
Agent Memory Isn't RAG: Why Vector Retrieval Falls Apart for Stateful Agents
Vector similarity search answers what text is topically related, but long-running agents need to know what is true right now. Conflating the two is why agents keep resurrecting overturned decisions.
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