Agent Stopping Conditions Explained
A practical, production-oriented explanation of agent stopping conditions, with examples, boundaries, trade-offs, and failure handling patterns.
Topic
#planning
15 articles — updated daily
Sequential vs Parallel Agent Execution
Compare sequential, parallel, and hybrid agent execution by dependencies, latency, cost, state transfer, synchronization, aggregation, and failure handling.
Multi-Agent Coordination Patterns Explained
Learn seven practical multi-agent coordination patterns and how they manage roles, ownership, state, handoffs, aggregation, conflicts, and stopping.
Agent Routing Patterns: How Agents Choose the Next Worker
Learn seven agent-routing patterns, from deterministic rules and classifiers to semantic, capability-aware, hierarchical, and fallback routing.
Orchestrator vs Supervisor vs Router in Multi-Agent Systems
Compare orchestrators, supervisor agents, and routers by purpose, decision ownership, state responsibility, delegation, routing, and workflow control.
AI Agent Architecture: Components and Data Flow
Understand modern AI-agent architecture from goals and instructions through reasoning, tools, observations, state updates, guardrails, and stopping.
Agent Workflows and Orchestration Explained
Learn how agent workflows and orchestration coordinate steps, dependencies, branches, parallel work, retries, checkpoints, tools, agents, and humans.
Agent Graphs and State Machines Explained
Learn how agent graphs and state machines make nodes, edges, branches, loops, checkpoints, transitions, retries, and terminal outcomes explicit.
Single-Agent vs Multi-Agent Systems
Learn how single-agent and multi-agent systems differ, what extra coordination costs, and how to choose the simplest architecture that works.
Reflection in AI Agents: How Agents Review, Correct, and Improve Their Work
Learn how AI agents use feedback, critique, and execution review to detect mistakes, revise their approach, and improve results without endless retry loops.
Planning in AI Agents: From Goals to Adaptive Action
Learn how AI agents turn goals into ordered tasks, account for dependencies and constraints, use tools, track progress, and replan when reality changes.
Reasoning in AI Agents: How Agents Decide What to Do Next
Learn how AI agents interpret goals, break down tasks, handle uncertainty, choose tools, reflect on results, and decide what to do next.
Anatomy of an AI Agent: The 9 Core Components
A beginner-friendly breakdown of the model, instructions, tools, memory, state, planning, feedback, guardrails, and execution loop inside an AI agent.
How AI Agents Work: The Complete Execution Loop
Follow the seven-stage execution loop that lets an AI agent reason, choose actions, use tools, learn from results, and keep working toward a goal.
What Is an AI Agent? A Practical Mental Model
A practical explanation of what makes an AI agent different from a chatbot or fixed workflow, and how the agent loop turns model reasoning into action.