Context Engineering: Why Relevant Context Beats Clever Prompts for AI Agents
For years, building with language models meant perfecting the words inside a prompt. That era is closing. As AI agents take on multi-step work—researching, calling APIs, writing files, waiting on human approvals—the decisive question is no longer “what should I write?” but “what information should enter the model’s attention at each step?” This is context..
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Small Language Models: Why SLMs Power Efficient AI Agents in 2026
For most of the last few years, building an AI agent meant wiring everything to the largest, most capable model available. That instinct is now being questioned. A growing body of research argues that for the repetitive, narrow tasks agents actually perform, a smaller model is not just adequate but often the better engineering choice…
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Agent-to-Agent Communication: How A2A Lets AI Agents Collaborate
Most AI agents today are powerful in isolation and awkward together. An agent that drafts a contract, one that checks it against company policy, and a third that files it in your CRM usually cannot talk to one another without a custom integration that breaks the moment a vendor updates its software. Agent-to-agent (A2A) communication..
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