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agent-systems

3 items tagged with "agent-systems"

Event

Build Your Own AI Coding Agent

We build a coding agent from scratch: tool calls, an agentic loop, a skills system that loads reusable instruction files on demand, and a final version running on PydanticAI. The agent can read and write files, run shell commands, search the codebase, and load specialized skills - all inside a structured Django project template. This workshop was originally delivered at MLCon 2026 Amsterdam. There is no video recording available. The agent you will build The architecture of the final agent: flowchart LR USER["User"] RUNNER["Runner<br/>ToyAIKit / OpenAI Responses"] LLM["LLM<br/>gpt-5.4-mini"] TOOLS["Tools<br/>read_file, write_file<br/>see_file_tree<br/>execute_bash_command<br/>search_in_files"] SKILL["SkillTool"] LOADER["SkillLoader"] SKILLS["SKILL.md files"] USER -->|prompt| RUNNER RUNNER -->|model call| LLM LLM -->|tool call or response| RUNNER RUNNER -->|execute tool| TOOLS RUNNER -->|load skill| SKILL SKILL --> LOADER LOADER -->|read| SKILLS RUNNER -->|answer| USER The agent runs inside a Django project template. It has five core tools for interacting with the filesystem and shell, plus a skills tool that loads extra instructions from SKILL.md files when the agent decides they are relevant. The workshop progresses through four parts: understanding tool calls, building the coding agent, adding skills, and porting to PydanticAI. Links Related workshops and resources: Previous coding-agent workshop - the v1 version without skills agent-skills workshop - the standalone skills workshop

Apr 21, 2026
Event

Skills.md from Scratch: Build a Skill-Driven Coding Agent

A newer, combined version of this workshop is available: Coding Agent with Skills. We start from the coding agent in the prerequisite workshop. We turn it into a general-purpose coding agent with two reusable behavior layers: skills and slash commands. The agent loads a skill when your request matches its description. You invoke a command with a leading slash, and the system renders it into a prompt before the agent acts. Links External resources for this workshop: Starting notebook GitHub fetch skill Prototype implementation Prerequisite coding-agent workshop ToyAIKit OpenCode OpenCode skills documentation AgentSkills spec The agent you will build By the end you'll have built a system that looks like this: flowchart LR USER["User"] RUNNER["ToyAIKit runner<br/>OpenAI Responses"] LLM["OpenAI model"] CODETOOLS["Coding tools<br/>read, write, tree, bash, search"] SKILLTOOL["skill(name) tool"] LOADER["SkillLoader"] SKILLS["skills/*/SKILL.md<br/>scripts and templates"] COMMANDS["commands/*.md"] COMMANDTOOL["execute_command(name, args)"] USER -->|plain request| RUNNER USER -->|/command| RUNNER RUNNER --> LLM RUNNER --> CODETOOLS RUNNER --> SKILLTOOL SKILLTOOL --> LOADER LOADER --> SKILLS RUNNER --> COMMANDTOOL COMMANDTOOL --> COMMANDS The project stays small enough to understand in a notebook, but it mirrors the pieces used by real coding agents. The coding tools let the model read, write, search, and run commands. The skill loader turns SKILL.md files with YAML frontmatter into tool-loadable instructions. The command loader turns markdown files like review.md or test.md into reusable prompt templates.

Jan 16, 2026
Event

Build a Course FAQ Agent with MCP, PydanticAI and OpenAI

We build a course FAQ assistant from the bottom up. First we expose a plain Python search(query) function to the OpenAI Responses API. Then we turn the same idea into a reusable agent loop and compare toyaikit, OpenAI Agents SDK, and PydanticAI. Finally we move the FAQ tools behind an MCP server. From there a notebook, PydanticAI, Cursor, and VS Code can all reach them. Links The main resources: AI Bootcamp: From RAG to Agents Prerequisite workshop: Building a Coding Agent Data Engineering Zoomcamp FAQ source document Parsed FAQ JSON FAQ parsing notebook The system you will build The final setup looks like this: flowchart LR NOTEBOOK["Jupyter notebook"] OPENAI["OpenAI Responses API"] FRAMEWORKS["Agents SDK<br/>PydanticAI"] MCPCLIENT["MCP clients<br/>toyaikit, PydanticAI, Cursor"] MCPSERVER["FastMCP server<br/>SSE or stdio"] TOOLS["FAQ tools<br/>search, add_entry"] INDEX["minsearch index<br/>FAQ JSON"] NOTEBOOK -->|function calling| OPENAI NOTEBOOK --> FRAMEWORKS FRAMEWORKS -->|tool calls| TOOLS NOTEBOOK -->|MCP client| MCPCLIENT MCPCLIENT -->|MCP protocol| MCPSERVER MCPSERVER --> TOOLS TOOLS --> INDEX The FAQ data comes from the Data Engineering Zoomcamp FAQ. The first half of the workshop keeps the tools inside the notebook so you can see the agent loop directly. The second half moves the same tools into mcp_faq/, which makes them reusable by any MCP client.

Sep 1, 2025