1046 posts · Page 23 of 88. ← Blog
LlamaIndex began as the fastest way to build RAG and has grown into a full data framework for LLM applications — connect your data, index it, retrieve it, and reason over it, with agents and workflows on top.
LlamaIndex began as the fastest way to build RAG and has grown into a full data framework for LLM applications — connect your data, index it, retrieve it, and reason over it, with agents and workflows on top.
CrewAI takes the most intuitive metaphor for multi-agent AI — a team of specialists with roles collaborating on a job — and makes it the programming model, which is both its great strength and the thing to be disciplined about.
CrewAI takes the most intuitive metaphor for multi-agent AI — a team of specialists with roles collaborating on a job — and makes it the programming model, which is both its strength and the thing to be disciplined about.
Synchronous request/response quietly welds your services together until a change in one breaks three others; event-driven architecture breaks that weld by making the event — a fact that happened — the thing services share.
Synchronous request/response quietly welds your services together until a change in one breaks three others; event-driven architecture breaks that weld by making the event — a fact that happened — the thing services share.
The first wave of self-evolving agents tuned one agent's memory and prompts; the frontier stops tuning a fixed agent and starts searching the space of agent designs itself.
The first wave of self-evolving agents tuned one agent's memory and prompts; the frontier stops tuning a fixed agent and starts searching the space of agent designs itself.
A DSPy signature is the contract that says what goes in and what comes out of a step — and by declaring the transformation instead of writing the prompt, it becomes something the optimizer can improve.
A DSPy signature declares what goes in and what comes out of a step — and by declaring the transformation instead of writing the prompt, it becomes something the optimizer can improve.
AI systems are non-deterministic, so "it looked good in the demo" is not a quality signal — evaluation is the control system for the entire lifecycle, and if it doesn't gate releases, nothing does.
AI is non-deterministic, so 'it looked good in the demo' is not a quality signal. Phase 4: evaluation as the control system that gates every release — measurable and gating, or it's decoration.
Agentic RAG is what you get when retrieval stops being a fixed pipeline step and becomes a set of decisions an agent reasons through — whether to retrieve, what to search for, from where, how many times, and whether to trust the result.
Agentic RAG is what you get when retrieval stops being a fixed pipeline step and becomes a set of decisions an agent reasons through — whether, what, from where, how many times, and whether to trust results.
The single biggest cost lever in most AI systems is not clever prompting — it is not using an expensive model for work a cheap one would do just as well.
The single biggest cost lever in most AI systems is not clever prompting — it is not using an expensive model for work a cheap one would do just as well. Right-sizing and routing models to tasks.
The system prompt is the one piece of context present on every single call, so how you structure its role, rules, and format is the highest-leverage writing in the whole system.
The system prompt is the one piece of context present on every call, so how you structure its role, rules, and format is the highest-leverage writing in the whole system.
Delegating real work between agents is rarely a quick round trip, so A2A makes the task a first-class object with an explicit lifecycle that survives long-running, interruptible, asynchronous collaboration.
Delegated work is rarely a quick round trip, so A2A makes the task a first-class object with an explicit lifecycle that survives long-running, interruptible, asynchronous collaboration.
Tools are the part of the Model Context Protocol that lets a model do things instead of just talk about them, and their design is what separates an agent that helps from one that flails.
Tools are the MCP primitive that lets a model act. How to define them, the tools/list and tools/call methods, results versus errors, and designing tools a model can actually use.
An agent whose action space is fixed can only ever recombine what it was given, but an agent that writes and banks its own skills grows more capable the longer it runs.
An agent that writes and banks its own verified skills grows more capable the longer it runs. Voyager's skill library and how to build a self-extending action space.