All Topics
Autonomous AI that gets things done
Agentic AI
Agentic AI systems don't just answer questions. They plan, execute multi-step tasks, use tools, and keep iterating until they reach a goal. Understanding agents means understanding where AI is actually headed in production, systems that act rather than just respond.
Lessons
1
What Makes an AI System Agentic?
The spectrum from chatbot to autonomous agent, where the line is, and what actually crosses it.
2
ReAct: Reasoning and Acting
The foundational pattern behind most agents: interleaved reasoning traces and tool calls that build on each other.
3
Beyond ReAct: Planning Patterns
ReAct isn't the only way to structure an agent. Compare it against Plan-and-Execute and Reflexion, and know when each is worth the extra complexity.
4
Agent Memory: In-Context, External, Procedural
How agents remember what they have done and what they know across multiple tasks.
5
Multi-Agent Systems
Orchestrator and worker patterns, agent communication, and when it makes sense to split work across agents.
6
Evaluating and Testing Agents
Metrics, tracing, and eval frameworks for measuring how reliable your agent actually is.
7
Agents in Production: Patterns and Pitfalls
Real-world architectures, common failure modes, human-in-the-loop design, and keeping costs under control.