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Agents

Concepts​

  1. Agent: An autonomous entity that perceives, reasons, and acts in an environment to achieve goals.
  2. Environment: The surrounding context or sandbox in which the agent operates and interacts.
  3. Perception: The process of interpreting sensory or environmental data to build situational awareness.
  4. State: The agent’s current internal condition or representation of the world.
  5. Memory: Storage of recent or historical information for continuity and learning.
  6. Large Language Models: Foundation models powering language understanding and generation.
  7. Reflex Agent: A simple type of agent that makes decisions based on predefined “condition-action” rules.
  8. Knowledge Base: Structured or unstructured data repository used by agents to inform decisions.
  9. CoT (Chain of Thought): A reasoning method where agents articulate intermediate steps for complex tasks.
  10. ReACT: A framework that combines step-by-step reasoning with direct environmental actions.
  11. Tools: APIs or external systems that agents use to augment their capabilities.
  12. Action: Any task or behavior executed by the agent as a result of its reasoning.
  13. Planning: Devising a sequence of actions to reach a specific goal.
  14. Orchestration: Coordinating multiple steps, tools, or agents to fulfill a task pipeline.
  15. Handoffs: The transfer of responsibilities or tasks between different agents.
  16. Multi-Agent System: A framework where multiple agents operate and collaborate in the same environment.
  17. Swarm: Emergent intelligent behavior from many agents following local rules without central control.
  18. Agent Debate: A mechanism where agents argue opposing views to refine or improve outcomes.
  19. Evaluation: Measuring the effectiveness or success of an agent’s actions and outcomes.
  20. Learning Loop: The cycle where agents improve performance by continuously learning from feedback or outcomes.

Building​

How to Build an AI Agent (7-Step Blueprint)

  1. System Prompt - Define the agent’s goals, role, and instructions. A thoughtful prompt shapes behavior from the ground up.
  2. LLM Selection - Pick your reasoning engine (e.g. GPT-4, Claude, Gemini) and tune it with parameters like temperature and max tokens.
  3. Tools - Give your agent abilities: from calling APIs to using other agents as tools. This is where agents move from “talking” to “doing”.
  4. Memory - Short-term and long-term memory (episodic, vector DBs, file stores) allow agents to remember, learn, and personalize over time.
  5. Orchestration - This is the brain behind the brain — workflows, triggers, A2A protocols, and message queues to structure intelligent behavior.
  6. User Interface - A good interface (chat, voice, web) brings your agent to life. It’s not just about function — it’s about trust and usability.
  7. AI Evaluations - Agents need feedback loops. Measure performance, learn from failure, and improve continuously.

Agentic AI Architectures​

Core agentic patterns such as ReAct, Reflection, Planning, and Tool Use

ReAct Pattern​

ReAct — Reasoning and Acting — is the most foundational agentic design pattern and the right default for most complex, unpredictable tasks. It combines chain-of-thought reasoning with external tool use in a continuous feedback loop.

The structure alternates between three phases:

  • Thought: the agent reasons about what to do next
  • Action: the agent invokes a tool, calls an API, or runs code
  • Observation: the agent processes the result and updates its plan

This repeats until the task is complete or a stopping condition is reached.

ReAct Pattern

Next Step: Adding Reflection to Improve Output Quality​

Reflection gives an agent the ability to evaluate and revise its own outputs before they reach the user. The structure is a generation-critique-refinement cycle: the agent produces an initial output, assesses it against defined quality criteria, and uses that assessment as the basis for revision. The cycle runs for a set number of iterations or until the output meets a defined threshold.

Reflection Pattern

Next Step: Making Tool Use a First-Class Architectural Decision​

Tool use is the pattern that turns an agent from a knowledge system into an action system. Without it, an agent has no current information, no access to external systems, and no ability to trigger actions in the real world. With it, an agent can call APIs, query databases, execute code, retrieve documents, and interact with software platforms. For almost every production agent handling real-world tasks, tool use is the foundation everything else builds upon.

Tool Use

Planning​

Planning is the pattern for tasks where complexity or coordination requirements are high enough that ad-hoc reasoning through a ReAct loop is not sufficient. Where ReAct improvises step by step, planning breaks the goal into ordered subtasks with explicit dependencies before execution begins.

There are two broad implementations:

  • Plan-and-Execute: an LLM generates a complete task plan, then a separate execution layer works through the steps.
  • Adaptive Planning: the agent generates a partial plan, executes it, and re-evaluates before generating the next steps.

Multi-Agent Collaboration​

Multi-agent systems distribute work across specialized agents, each with focused expertise, a specific tool set, and a clearly defined role. A coordinator manages routing and synthesis; specialists handle what they are optimized for.

Multi-Agent Collaboration

The benefits are real — better output quality, independent improvability of each agent, and more scalable architecture — but so is the coordination complexity. Getting this right requires answering key questions early.

Ownership — which agent has write authority over shared state — must be defined explicitly. Routing logic determines whether the coordinator uses an LLM or deterministic rules. Most production systems use a hybrid approach. Orchestration topology shapes how agents interact:

The Roadmap to Mastering Agentic AI Design Patterns

Examples / Learning​

References​