Foundational Concepts
What agents are, the agent loop, autonomy spectrum, and core building blocks.
Action Space Design
The action space defines everything an agent can do — the set of tools, their parameters, and their constraints — and its design is a critical balancing act between expressiveness (enough tools to be useful) and tractability (few enough tools for the LLM to select correctly).
The Agent Loop
The agent loop is the observe-think-act cycle that drives all AI agent behavior — a repeated process where the agent perceives its environment, reasons about the next step, executes an action, and feeds the result back into the next iteration.
Agent State Management
Agent state management is the practice of tracking, organizing, and maintaining all information an agent needs across turns — including conversation history, working memory, task progress, and environmental snapshots — because the LLM itself retains nothing between calls.
Agent vs. Workflow
Agents and workflows represent two fundamentally different approaches to automating tasks — agents use LLM reasoning to dynamically decide each step, while workflows follow predetermined coded paths — and choosing between them is one of the most consequential architectural decisions in building AI systems.
The Autonomy Spectrum
AI agent systems range from human-driven copilots that suggest completions to fully autonomous agents that independently execute multi-step tasks, and choosing the right level of autonomy is a core design decision with direct implications for safety, cost, and user trust.
Determinism vs. Stochasticity
LLM-based agents are inherently non-deterministic — the same input can produce different outputs across runs due to temperature sampling, floating-point arithmetic, and tool output variability — and managing this stochasticity requires deliberate strategies while recognizing that some randomness is actually beneficial.
Environment and Observations
An agent’s environment encompasses everything it can perceive and interact with — tool outputs, API responses, file systems, user messages, and system state — and the quality of an agent’s behavior depends heavily on how observations from this environment are processed, filtered, and presented to the LLM.
Goal Specification
Goal specification is the process of translating human intent into machine-actionable objectives — through system prompts, user instructions, success criteria, and multi-turn refinement — and the gap between what the human means and what the agent understands is the single largest source of agent failure.
LLM as Reasoning Engine
The large language model serves as the “brain” of an AI agent — providing language understanding, common-sense reasoning, planning, and few-shot learning — but it is a stateless, probabilistic engine with fundamental limitations that the agent architecture must compensate for.
What Is an AI Agent?
An AI agent is a software system that uses a language model to autonomously perceive its environment, reason about goals, and take actions — going beyond simple question-answering into sustained, goal-directed behavior.