agents

Working with AI agents that plan, execute and refine code collaboratively, far beyond simple autocomplete. What is agentic coding? Agentic coding involves AI systems that can: Break down complex tasks into steps Execute multi-step workflows autonomously Read and understand existing code Make decisions about implementation approaches Iterate and refine based on feedback or errors This is distinct from autocomplete or chat-based assistance. The agent operates with more autonomy and context awareness. ...

Competitive Agentic Forking is a software development workflow pattern where specific tasks are “forked” to multiple independent AI agents or models. Instead of relying on a single agent, the system spawns parallel competitors that attempt the same task. Their outputs are then evaluated, compared, and either selected or merged. This approach brings the competitive evaluation model popularized by lmarena.ai/leaderboard directly into development workflows—not as external benchmarking, but as native process integration. ...

A context graph captures decision traces: the evidence, actions, forks, and approvals behind an outcome. It matters when the outcome alone is not enough to answer questions like: Have we seen this before? Why did we choose that? What did we try? What changed the decision? In that sense, a context graph is not just a graph of entities or documents. It is a way to preserve the path of work in machine-usable form so future humans and agents can inspect and reuse it. ...