1
Define the task
Set boundaries, completion criteria and human checkpoints.
Agent engineering
AI Agent Development
Build tool-using agents with MCP, function calling, retrieval, memory and reliable multi-step execution patterns.
Move from a prompt demo to an observable agent workflow.
Structured learning path
Learn in the order developers build.
Start with the decision, move into implementation and finish with production checks.
2
Connect tools
Use tool calling or MCP with strict schemas and permissions.
3
Add context
Combine retrieval, memory and stable cached instructions.
4
Evaluate runs
Measure completion, corrections, latency and cost per task.
Core guides
Build topical authority, not article islands.
Foundation01
AI Agent API Fundamentals
The request loop behind planning, tools and completion.
Open guideProtocol02
MCP Integration Guide
Connect agents to tools and data with Model Context Protocol.
Open guideTools03
Tool Calling Patterns
Schemas, validation, retries and safe execution.
Open guideContext04
RAG for Agents
Retrieve the minimum useful context for each step.
Open guideMemory05
Agent Memory Design
Separate working memory, user memory and durable state.
Open guide Use case
Build a repository agent
Give an agent a bounded goal, repository tools and explicit verification steps before it reports completion.
Read-only discovery first
Scoped write permissions
Tool-result validation
Build and test evidence
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Explore every growth topic.
AI API Guides
Go from model shortlist to a working production request.
LLM Infrastructure
Build an AI request layer that survives provider and model changes.
AI Agent
Move from a prompt demo to an observable agent workflow.
Cost Optimization
Lower cost without hiding quality or reliability trade-offs.
Model Comparison
Select a model based on the job, not the leaderboard headline.
Tutorials
Copy a working pattern, understand it and adapt it to your application.
AEO-ready answers
Frequently asked questions
What makes an AI agent different from a chatbot?
An agent can choose and execute tools across multiple steps while tracking state toward a defined outcome.
When should an agent use memory?
Use memory only when information must persist across steps or sessions; keep transient reasoning and durable user facts separate.