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"Agent ≠ multi-turn dialogue." Breaks down the four components of an AI Agent—planning, memory, tools, execution—and explains ReAct vs. Plan-Execute, short/long-term memory design, function calling engineering points, and pushing an Agent from demo to production via loop control, error recovery, and observability.
Putting documents in a vector database and using top-k retrieval is only RAG's "hello world." This article covers four levers for improving RAG quality: chunking, hybrid retrieval, reranking, and query rewriting, plus a reproducible evaluation method that takes RAG from "barely usable" to genuinely accurate.
The most common problem in multi-agent systems isn't model capability, but incomplete inputs, unclear responsibility, and unverifiable results after task delegation. This article covers delegation contracts, result schemas, budget propagation, and recovery mechanisms.
An agent's evaluation set determines what the team will optimize for. This article covers how to sample from real workflows, annotate observable outcomes, cover failure paths, and maintain versions, so evaluation results actually guide engineering decisions.
Technical trends, engineering practices, and independent developer perspectives around AI Agents and large language models.
Covers tech trends, engineering practices, and indie developer perspectives around large models, RAG, and AI.
Technical trends, engineering practices, and an independent developer's perspective on LLMs and open source.
Technical trends, engineering practices, and an independent developer's perspective on RAG and large language models.
Around the technical trends, engineering practices, and indie-developer perspectives of large models and AI.
Technical trends, engineering practices, and an independent developer perspective on RAG and large language models.
When an Agent errors, it should not mechanically retry. This article provides a method to decide the recovery path based on error retryability, idempotency, risk level, and evidence completeness, and explains when to degrade or hand off to a human.
Covers tech trends, engineering practices, and indie developer perspectives around large models and RAG.