AI Agent Learning
Build AI agents from scratch with OpenAI Java SDK — 14 progressive lessons from a basic chat loop to multi-agent collaboration
The Core Pattern
Every AI agent shares the same kernel: call the model, execute tools, feed results back. Each lesson adds one new mechanism on top.
while (true) {
response = client.chat().completions().create(params);
if (response.toolCalls().isEmpty())
break;
for (toolCall : response.toolCalls()) {
result = dispatch(toolCall.name(), toolCall.arguments());
messages.add(result);
}
}Message Growth
Watch the messages array grow as the agent loop executes
Learning Path
14 progressive lessons, from a simple chat loop to autonomous multi-agent execution
Basic Chat Loop
The simplest agent is just a while loop calling the model
Tool Use
Function calling turns a chat model into an agent that can act
Tools
The loop stays the same; new tools register into the dispatch map
TodoWrite
An agent without a plan drifts; list the steps first, then execute
Subagents
Subagents use independent context, keeping the main conversation clean
Skills
Inject knowledge via tool_result when needed, not upfront in the system prompt
Compact
Context will fill up; three-layer compression enables infinite sessions
Tasks
A file-based task graph with ordering, parallelism, and dependencies
Background Tasks
Run slow operations in the background; the agent keeps thinking ahead
Agent Teams
When one agent can't finish, delegate to persistent teammates via async mailboxes
Team Protocols
One request-response pattern drives all team negotiation
Autonomous Agents
Teammates scan the board and claim tasks themselves; no need for assignment
Worktree Isolation
Each works in its own directory; tasks manage goals, worktrees manage directories
Full Reference Agent
The capstone combines every mechanism from Lessons 1–12 into one agent
Architectural Layers
Five orthogonal concerns that compose into a complete agent system