AI Agents
Autonomous systems that reason, plan, and act.
- AI Agents
Explore how AI agents autonomously reason, plan, and take actions to accomplish goals.
- Agentic AI
Understand the paradigm shift from prompt-response AI to systems that act autonomously.
- Multi-Agent Systems
Learn how multiple AI agents collaborate, delegate, and coordinate to solve complex problems.
- Agent Architectures
ReAct, Plan-and-Execute, reflexion, and other architectural patterns for building reliable AI agents.
- Planning
How AI agents decompose goals into steps, create plans, and adapt when things go wrong.
- Memory
Short-term, long-term, and episodic memory patterns for AI agents that need to remember across sessions.
- Agentic RAG
RAG systems where agents decide what to retrieve, when to search, and how to synthesize multi-step answers.
- Workflows vs Agents
When to use deterministic LLM workflows vs autonomous agents - and why most production systems are hybrids.
- Human in the Loop
Approval workflows, interrupt/resume patterns, and human review gates that keep production AI agents safe and controllable.
- Durable Execution
Checkpointing, persistence, retries, and resumable workflows for long-running AI agents that survive failures and interruptions.
- Multi-Agent Handoffs
Delegation, routing, and specialist-agent handoffs that transfer control and context between agents in a multi-agent system.
- Agent Evaluation
Evaluate AI agents on task success, trajectory quality, tool-call accuracy, groundedness, safety, latency, cost, and production traces.
- Guardrails
Safety constraints and validation on AI inputs and outputs — content filters, schema checks, and policy enforcement in agent and app pipelines.