Autonomous systems that combine language models with reasoning, memory, and tool use to execute complex multi-step tasks with minimal human intervention.
An AI agent is a system that uses a language model as its reasoning core, but goes beyond answering questions: it can plan, execute actions, observe results, and adjust its behavior in a closed loop.
The fundamental difference between a chatbot and an agent is autonomy. A chatbot answers a question and waits for the next one. An agent receives a goal, decomposes the problem, decides which tools to use, executes intermediate steps, and delivers a result — all with minimal human intervention.
A typical agent has four components:
The most common pattern. The agent alternates between reasoning about what to do and executing actions:
Thought: I need to look up the current price of AAPL
Action: search_price(ticker="AAPL")
Observation: $187.50
Thought: Now I can respond to the user
Response: The current price of AAPL is $187.50
The agent first creates a complete plan and then executes each step. Useful for complex tasks where order matters.
Multiple specialized agents collaborate to solve a problem. Each agent has a specific role (researcher, writer, reviewer) and they communicate with each other.
| Framework | Language | Focus |
|---|---|---|
| LangGraph | Python | State graphs for agentic flows |
| CrewAI | Python | Agent teams with roles |
| Strands Agents | Python | Modular agents from AWS |
| AutoGen | Python | Multi-agent conversations |
| Vercel AI SDK | TypeScript | Agents in web applications |
Agents represent the next leap in AI applications: from systems that answer questions to systems that complete tasks. Understanding their execution patterns, limitations, and available frameworks is essential for building applications that go beyond chat.
Field of computer science dedicated to creating systems capable of performing tasks that normally require human intelligence, from reasoning and perception to language generation.
Open protocol created by Anthropic that standardizes how AI applications connect with external tools, data, and services through a universal interface.
AWS serverless service providing access to foundation models from multiple providers (Anthropic, Meta, Mistral, Amazon) via unified API, without managing ML infrastructure.
Key takeaways from Nate B Jones' second brain series — from the original 8 building blocks to Open Brain (Postgres + MCP), the two-door principle, and the implementation gap.
Key takeaways from Dr. Werner Vogels' final keynote at AWS re:Invent 2025, where he presents the Renaissance Developer framework and argues why AI will not replace developers who evolve.
Findings from manual review of PR
Key takeaways from Dario Amodei's essay on civilizational risks of powerful AI and how to confront them.
Chronicle of building a second brain with a knowledge graph, bilingual pipeline, and agent endpoints — in days, not weeks, and what that teaches about the gap between theory and working systems.
Demonstration of dual-interface architecture where the same business logic serves both a traditional web application and an MCP server for AI tools.
MCP server for analyzing git repository metrics and understanding team health. Published on npm as @jonmatum/git-metrics-mcp-server.
Three-agent system that automates the bilingual MDX content lifecycle: deterministic QA auditing, surgical fixes, and full upgrades — all orchestrated with Strands Agents, Claude Sonnet 4 on Amazon Bedrock, and GitHub Actions with a diamond workflow pattern.
Design strategies and patterns for AI agents to select, invoke, and combine external tools effectively to complete complex tasks.
Open source SDK from AWS for building AI agents with a model-driven approach. Functional agents in a few lines of code, with multi-model support, custom tools, MCP, multi-agent, and built-in observability.
Information retrieval technique that uses vector embeddings to find results by meaning, not just exact keyword matching.
Architectural pattern that combines information retrieval from external sources with LLM text generation, reducing hallucinations and keeping knowledge current without retraining the model.
The discipline of designing effective instructions for language models, combining clarity, structure, and examples to obtain consistent, high-quality responses.
Architectures where multiple specialized AI agents collaborate, compete, or coordinate to solve complex problems that exceed a single agent's capability.
LLM capability to generate structured calls to external functions based on natural language, enabling integration with APIs, databases, and real-world tools.
Prompting technique that improves LLM reasoning by asking them to decompose complex problems into explicit intermediate steps before reaching a conclusion.
Field dedicated to ensuring artificial intelligence systems behave safely, aligned with human values, and predictably, minimizing risks of harm.
Tools using LLMs to help developers write, understand, debug, and refactor code, from autocomplete to agents that implement complete features.
Design patterns where AI agents execute complex multi-step tasks autonomously, combining reasoning, tool use, and iterative decision-making.