AI Agents

Autonomous AI agents degrade over long-horizon tasks through accumulated context pollution, not single failures. What agent drift is, why it happens, and how to architect against it.

Build AI agents from the inside out — start with behavior in CLAUDE.md, add capabilities via MCP, then wrap in code with the Claude Agent SDK.

Repowire is a pull-based mesh network that lets Claude Code sessions communicate across repositories in real-time, replacing stale docs with live agent-to-agent queries.

Multi-repo coding agent workflows turn you into the message bus. When work spans repositories, you become the slowdown and the source of errors.

Agents are stateful logic, not stateless apps. The cloud primitives — durable execution, agent identity, MCP — that AI agents need to run reliably.

AI agent memory management mirrors database design. Agent state needs persistence, retries, and checkpointing — the same problems backend systems solved.

Code is becoming a transient artifact — AI-generated bytecode. The future of programming belongs to Architects of Intent, not writers of loops.

Torale monitors the web so you don't have to. How ambient AI turns passive monitoring into proactive intelligence — and how Gemini made it possible.

Explore how agentic MLOps uses AI agents and MCP to autonomously handle data drift, model deployment, and ML pipeline orchestration across your entire stack.

Learn how the A2A Protocol enables secure AI agent communication. Covers Google's open standard, MCP differences, authentication, monetization, orchestration, and Python examples.

A practical comparison of AI agent frameworks — OpenAI Agents SDK, Google ADK, AWS Bedrock, and Smol Agents — covering architecture, multi-agent systems, and emerging trends.