AI Agent Tutorials

Stand up a battle-ready development workspace for agent work: Python, virtual environments, dependency pinning, observability hooks, and policy-ready configuration.

Design the conversation loop, configuration system, logging, and CLI harness that turn a sandbox into a working, testable agent.

Give your agent durable memory: episodic storage, semantic embeddings, context compression, and retrieval hooks that keep conversations coherent.

Teach your agent to act: define structured tool schemas, safeguard API calls, and execute workflows through a planner.

Wrap the series by adding simulation suites, CI/CD pipelines, observability, and deployment patterns for your agent.

Lay the groundwork for a TypeScript/Node.js assistant with disciplined architecture, tooling, and observability before writing a single prompt.

Implement the assistant nucleus: configuration-driven services, OpenAI client, conversation store, retries, and an ergonomic CLI.

Persist conversations in SQLite, summarize turns, embed highlights, and retrieve relevant context automatically.

Expose calendar, email, and docs integrations through a governed tool registry, planner, and executor.

Finish the series with simulation packs, CI pipelines, Dockerized deployment, and runbooks that keep the assistant reliable.

Choose APIs with intention, wire up OAuth and API keys safely, and keep your agents online with rate limiting plus observability guardrails.

Design Slack-native agents with resilient installs, real-time routing, interactive workflows, and shared Node/Python patterns that survive rate limits and audits.

Stand up Discord-native agents that handle intents, slash commands, safety tooling, and observability with mirrored Node/Python implementations.

Let your agent trigger 6,000+ SaaS workflows safely by pairing Zapier webhooks, OAuth connections, and dual-language signing guards.

Chain Slack, Discord, and Zapier integrations through resilient workflow engines with shared Node/Python orchestration patterns.

Master robust authentication for AI agents using JWT tokens and OAuth flows. Learn secure token management, session handling, and production-ready authentication patterns.

Master advanced authorization systems for AI agents with role-based access control, dynamic permissions, and attribute-based security. Learn to implement enterprise-grade access control that scales.

Master comprehensive data privacy and encryption strategies for AI agents. Learn GDPR compliance, end-to-end encryption, and secure data handling that protects user information and builds trust.

Master enterprise-grade API key management for AI agents. Learn secure storage, rotation strategies, access control, and monitoring that protects your keys and prevents costly security breaches.

Master comprehensive security auditing and monitoring for AI agents. Learn automated vulnerability scanning, threat detection, compliance reporting, and incident response that keeps your AI agent secure in production.

Master the art of designing effective agent roles for collaborative multi-agent systems. Learn architectural patterns, role definitions, and coordination strategies for scalable AI agent teams.

Master advanced communication protocols for multi-agent systems. Learn message passing patterns, event-driven architectures, and reliable communication strategies that ensure seamless agent collaboration.

Master the implementation of modular, production-ready AI agents with robust communication, error handling, and lifecycle management. Build agents that work seamlessly in collaborative systems.

Master orchestration strategies for multi-agent systems. Learn patterns, workflows, conflict resolution, and production-ready orchestration techniques that ensure collaborative success.

Master enterprise-scale multi-agent systems with real-world case studies, performance optimization, and production deployment strategies. Learn from successful implementations and avoid common scaling pitfalls.

Master the art of preparing high-quality training data for fine-tuning large language models. Learn data collection, cleaning, formatting, and quality assurance techniques for optimal model performance.

Master OpenAI's fine-tuning platform to create specialized AI agents. Learn hyperparameter optimization, training job management, and advanced evaluation techniques for production-ready models.

Master Hugging Face fine-tuning for complete control over your AI agent models. Learn advanced training techniques, custom model architectures, and cost-effective alternatives to commercial APIs.

Master the deployment of fine-tuned models for production AI agents. Learn hosting strategies, performance optimization, and seamless integration patterns that bring your custom models to life.

Integrate your fine-tuned model with your agent and troubleshoot common issues for optimal performance.

Learn how to prepare your AI agent project for cloud deployment. Covers code cleanup, environment variables, security hardening, monitoring setup, and production best practices.

Master Vercel deployment for AI agents with serverless functions, edge computing, and automatic scaling. Learn production-ready deployment strategies with monitoring and optimization.

Master AWS deployment for AI agents with Lambda functions, API Gateway, and enterprise-grade infrastructure. Learn scalable, cost-effective deployment strategies with comprehensive monitoring.

Master Azure deployment for AI agents with App Service, Functions, and enterprise integration. Learn Microsoft's cloud ecosystem and build scalable, enterprise-ready AI agent deployments.

Secure, monitor, and scale your cloud-deployed AI agent for production use. Covers HTTPS, analytics, and scaling strategies.

Build an observability lab for AI agents with traces, replay logs, cost metrics, and automated tests using Node and Python tooling.

Design an agent failure taxonomy, automate signal collection, and route alerts to humans with Node and Python reference code.

Simulate agent traffic, inject failures, and benchmark SLOs using load tests, chaos toggles, and regression suites in Node and Python.

Turn incidents into improvements with postmortem templates, quality backlogs, and automated experiment tracking.