Agents

Across five revisions the Model Context Protocol has steadily reduced the set of responsibilities it claims for itself, transferring authentication to the identity provider, client identity to the domain name system, consent to the administrator, and session state and routing to the application and the gateway. What the reduction leaves behind is a single trust boundary for which no better owner exists, and the attacks that have succeeded in practice are concentrated there.

An agent can be trusted with exactly as much authority as your policy engine can scope, enforce, and afterwards prove. Which makes authorization not the tax you pay to ship agents, but the thing that decides how much power they can ever be given. A deep dive into PDP/PEP architecture, Cedar and Rego, partial evaluation, workload identity, and why the credential an agent presents should be minted for the action rather than held for the deployment.

My son was using chatbots to finish his homework instead of learning from it. So I built him a tutor on a network-caged agent platform: one that guides instead of answers, reads his Tamil worksheets, runs scheduled drills, and is safe by architecture rather than by prompt.

What it takes to turn an open 3B vision-language model into a GUI grounding agent for about $15: visual grounding, LoRA and QLoRA, and a verifiable-reward GRPO recipe, where SFT does the heavy lifting and RL rewards the whole target, not just its center.

Most enterprises have bought an AI coding agent and are stuck. The ones generating real productivity gains didn’t win by picking a better model. They built a platform around the agent. This post walks through the five control-plane responsibilities that separate the 11% of AI-native orgs from the 95% reporting zero ROI, grounded in public deployments from Block, Shopify, Atlassian, Airbnb, and others.

An interactive technical breakdown of Claude Code’s architecture — from the query loop and five compaction mechanisms to the permission pipeline and feature flags. Based on source code analysis of ~1,884 TypeScript files.

Production AI agents face infrastructure problems that framework-level code cannot solve: state loss on crashes, LLM API flakiness, debugging non-deterministic behavior, and coordinating human approvals across hours-long runs. This post walks through Temporal’s durable execution model and why companies like OpenAI chose it for their agent infrastructure.

An interactive visual exploration of OpenClaw — the open-source AI agent that broke GitHub. Explore its three-layer architecture, two key primitives, memory system, and composable system prompt.

A deep dive into the architecture, design patterns, and engineering decisions behind production-grade agentic code assist solutions. By dissecting OpenHands, we uncover how to build AI agents that safely execute code, manage complex state, and operate reliably in production.