1046 posts · Page 21 of 88. ← Blog
A CrewAI agent is defined less by code than by three sentences — its role, goal, and backstory — and getting those right is the highest-leverage thing you do, because they are the prompt that shapes everything the agent does.
A CrewAI agent is defined less by code than by three sentences — its role, goal, and backstory — and getting those right is the highest-leverage thing you do, because they are the prompt that shapes everything the agent does.
Most AI architecture debates are settled by hype, familiarity, or whoever spoke last — this series settles them by requirements and trade-offs, starting with the meta-framework that every specific decision reduces to.
Most AI architecture debates are settled by hype or familiarity; this series settles them by requirements and trade-offs, starting with the meta-framework every specific decision reduces to.
Almost everything Kafka does follows from one deceptively simple idea — an append-only, ordered, durable log — and once that clicks, topics, partitions, and offsets stop being jargon and become obvious.
Almost everything Kafka does follows from one deceptively simple idea — an append-only, ordered, durable log — and once that clicks, topics, partitions, and offsets stop being jargon and become obvious.
The most striking frontier result is a meta-agent that writes agents — defining them as code, testing them, archiving the good ones, and inventing architectures that outperform the best humans hand-built.
The most striking frontier result is a meta-agent that writes agents — defining them as code, testing them, archiving the good ones, and inventing architectures that outperform the best humans hand-built.
If a signature says what a step does, a module says how to get the model to do it — and because modules are parameterized, swapping one for another changes the reasoning strategy without touching your intent.
If a signature says what a step does, a module says how to get the model to do it — Predict, ChainOfThought, ReAct — and because modules are parameterized, swapping one changes the strategy without touching your intent.
A model that scores well in evaluation still has to serve real traffic within a latency budget, isolate tenants, plan for capacity, and enforce safety in the request path — and the guardrails have to be inline, not a filter someone can route around.
A model that scores well still has to serve traffic within a latency budget, isolate tenants, plan capacity, and enforce safety in the request path — inline, not as a filter someone can route around. Phase 5.
The user's question is written to be asked, not to be searched, so the first thing an agentic RAG system should do is turn that question into queries that actually retrieve well.
The user's question is written to be asked, not searched, so the first thing an agentic RAG system should do is turn that question into queries that actually retrieve well.
Since you pay for every input token, the fastest way to cut the cost of a call without changing the model is to send fewer tokens — and most production prompts are carrying far more than they need.
Since you pay for every input token, the fastest way to cut the cost of a call without changing the model is to send fewer tokens — and most production prompts carry far more than they need.
Retrieval is how you put external, current knowledge into a model's window, and doing it well is a context-engineering problem — the goal is not the most documents, but the right ones.
Retrieval is how you put external, current knowledge into a model's window, and doing it well is a context-engineering problem — the goal is not the most documents, but the right ones.
Agents need to exchange more than plain strings — instructions, files, images, structured data, and finished deliverables — and A2A's content model handles all of it with three composable objects.
Agents exchange more than plain strings — instructions, files, images, structured data, and finished deliverables. A2A's content model handles all of it with three composable objects.
Tools let a model act, but resources and prompts are how a Model Context Protocol server feeds it the right context and gives users repeatable ways to invoke it.
Beyond tools, MCP servers expose resources (read-only context by URI) and prompts (reusable templates). What each is for, how they appear on the wire, and how to choose.
Asking a model to check its own work sounds like free improvement, but whether it actually helps depends entirely on where the feedback comes from — and getting this wrong is the most common way self-evolving agents fool themselves.
Self-critique is tempting but dangerous: without a real external signal, models often fail to self-correct and can get worse. Where self-critique works and where it drifts.