Instead of triggering tools one at a time across turns, agents are collapsing their tool catalog into a single exec(code) tool—chaining, filtering, and awaiting actions inside an isolated sandbox.
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Try itUse Google Research's Harness² to understand the pieces around an AI model, how recursive refinement works, and how to evaluate changes on untouched tasks.
Your PDFs in a chat window is not a RAG system The gap between one people trust and one they quietly stop using is entirely in the retrieval step. Somebody in your company has already tried the cheap …
The Missing Cent: A Beginner’s Guide to Harness Engineering. AI agents can write code, but can you trust it? Part 1 of my Harness Engineering From Scratch series. This is Part 1 of my series …
It called it "Spark Copilot", and it works similarly to the real thing.
...explained visually (with tradeoffs in production).
A demonstration of how to use MLflow AI Gateway to manage coding agent sprawl.
Turn a brittle AGENTS.md into useful project guidance. Explain the reason behind a rule, keep hard checks in code, and make approval boundaries explicit.
The article argues that reliable agentic AI architecture depends on a context engine that selects, injects, compresses, and expires information before it reaches the model.
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