Agent Latency Explained
Why multi-step agents feel slow, where elapsed time accumulates, and how to improve speed without breaking the task.
Topic
#agent-loop
25 articles — updated daily
Agent Stopping Conditions Explained
A practical, production-oriented explanation of agent stopping conditions, with examples, boundaries, trade-offs, and failure handling patterns.
Handling Tool Failures in AI Agents
A practical, production-oriented explanation of tool failure handling, with examples, boundaries, trade-offs, and failure handling patterns.
Idempotency in Agent Workflows
A practical, production-oriented explanation of idempotency in agent workflows, with examples, boundaries, trade-offs, and failure handling patterns.
Retries, Timeouts, and Failure Recovery in AI Agents
A practical, production-oriented explanation of retries, timeouts, and failure recovery, with examples, boundaries, trade-offs, and failure handling patterns.
Reliable AI Agent Architecture
A practical, production-oriented explanation of reliable AI agent architecture, with examples, boundaries, trade-offs, and failure handling patterns.
LLM Evaluation vs Agent Evaluation
LLM evaluation scores model outputs; agent evaluation measures the whole goal-directed system, including tools, state, constraints, reliability, latency, and cost.
AI Agent Evaluation Explained
Agent evaluation measures task outcomes, trajectories, tool behavior, constraints, safety, reliability, latency, and cost—not only final prose.
Agent Traces and Trajectories
Learn how traces and trajectories represent observable agent execution without requiring storage or exposure of private chain-of-thought.
Sequential vs Parallel Agent Execution
Compare sequential, parallel, and hybrid agent execution by dependencies, latency, cost, state transfer, synchronization, aggregation, and failure handling.
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 Handoffs, Delegation, and Sub-Agents
Learn how delegation, handoffs, and sub-agents divide work while preserving task ownership, context, state, permissions, and reliable result contracts.
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.
Build Your First RAG Agent
Build a framework-neutral RAG agent with a controlled retrieval tool, attributable evidence, bounded loops, citation checks, traces, and layered evaluation.
RAG vs AI Agent: What’s the Difference?
A decision-focused comparison of RAG knowledge retrieval and AI-agent execution, including when a simple RAG pipeline is enough and when an agent is justified.
Build Your First AI Agent
Build a genuine AI task agent in plain Python with tool calling, observations, state, guardrails, logging, error handling, and tests.
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.
Memory in AI Agents: How Agents Remember, Retrieve, and Forget
Learn how AI agent memory works, from context windows and working memory to persistent stores, retrieval, updating, forgetting, and memory quality.
Tool Use in AI Agents: How Agents Act Beyond the Model
Learn how AI agents select tools, prepare arguments, execute functions and APIs, observe results, recover from errors, and stay within safe permission boundaries.
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.