Deployment & Scaling
How to Deploy AI Agents with FastAPI and Redis Queue for Background Tasks
Run AI agent tool calls in the background using FastAPI and Redis so long-running jobs never block your API clients or timeout mid-execution.
Agent Design Patterns
How to Build Agents
Practical guides and tutorials for developers and AI engineers who want to design, integrate, and deploy reliable AI agents. Explore architecture patterns, tool use, evaluation techniques, and production deployment strategies.
- Agent Design Patterns
- LLM Integration & APIs
- Tool Use & Function Calling
Articles
Latest Articles
New guides and tutorials on AI agent development, covering design patterns, integration, and deployment.
LLM Integration & APIs
How to Build a Multi-Provider LLM Client for Agents
Learn to abstract provider-specific endpoints, schemas, and streaming formats into a single client interface to reduce vendor lock-in and manage model failures.
Multi-Agent Systems
How to Implement a Supervisor-Worker Multi-Agent Pattern
Learn how to structure a supervisor agent to delegate subtasks to specialized workers, aggregate results, and manage the overall execution flow.
Multi-Agent Systems
How to Build a Researcher-Writer Agent Handoff Loop for Documentation
Set up a multi-agent workflow where one agent gathers sources and another drafts, with a defined handoff condition.
Tool Use & Function Calling
How to Validate LLM Tool Calls with JSON Schema Before Execution
Set up schema validation for agent tool calls to prevent malformed arguments and unsafe API requests.
Tool Use & Function Calling
Top 4 MCP Servers for AI Agent Tooling
Explore Model Context Protocol servers that provide standardized, secure tools for agents to interact with files, databases, and external APIs, including GitHub, Notion, Supabase, and local filesystems.
FAQ
Frequently Asked Questions
Common questions about learning AI agent development, evaluation, hosting, and updates.
What are AI agents and how do they differ from simple LLM calls?
AI agents combine large language models with tools, memory, and reasoning loops to accomplish goals autonomously. They go beyond single prompts by planning, using external context, and iterating toward a result.
What skills do I need to build AI agents?
A solid foundation in Python, API integrations, and LLM prompt engineering is helpful. Familiarity with function calling, vector databases, and cloud deployment platforms accelerates development.
How do I evaluate an AI agent's performance?
Use a mix of automated metrics, such as task success rate and latency, plus human review of edge cases. Build a test suite with representative scenarios before deploying to production.
Where can I host my AI agent?
Cloud providers like Google Cloud, AWS, and Azure offer managed AI services, while container platforms such as Kubernetes support scalable custom deployments. Choose based on latency, cost, and control needs.
How often is this site updated?
We publish new practical guides as the AI agent landscape evolves. Check the articles page regularly for the latest tutorials and best practices.