Coding agents spend most of their budget on file and symbol discovery, not on actual reasoning. Here is how precomputed context graphs change that, and why it matters for every team shipping agentic dev tools.
Most ‘AI agents’ demos hide a complex runtime underneath. Here is what actually runs in production — the planner, the tools, the memory layer, the guardrails, and the observability stack that holds it all together.
AI agents aren’t chatbots. They execute code, hold credentials, and call APIs on their own. Here’s why that breaks traditional security models — and what teams are doing about it.
Harness separates its AI coding agent into a lightweight planner and a sandboxed executor. Here’s why that split matters for latency, security, and observability.
A deep dive into the architecture of autonomous memory systems for distributed AI agents, covering tiered storage, consistency models, and real-world orchestration patterns.