In 2025, Agentic AI is changing the game. Unlike simple chatbots, agentic systems can plan, collaborate, and act autonomously—like a team of specialists working toward a goal. I recently completed the AI Engineer Agentic Track to master these skills, learning how to design, orchestrate, and deploy real-world agentic systems.

Frameworks I Worked With

• OpenAI Agents SDK – building multi-step LLM workflows.
• CrewAI – managing agent teams for research, software, and business tasks.
• LangGraph – orchestrating agents and tools inside browsers and apps.
• AutoGen – creating agents that can build and deploy other agents.

Key Learnings

1. Agent Roles and Responsibilities

I learned more about how to define each agent, the bounderies and help you have input/outputs

2. Orchestration Patterns

I explored three patterns:

• Manager: linear workflows with a single controller.
• Pipeline: stages passing artifacts downstream for deterministic tasks.
• Blackboard: shared workspace for complex, multi-step collaboration.

3. Tool Integration and Function Calling

Agents interact with APIs, databases, scrapers, and other tools. Structured outputs and typed requests reduce hallucinations and errors.

4. Multi-LLM Strategies

Different models have strengths in coding, reasoning, summarization, and cost. I learned to assign roles to specific models, use cheaper ones for drafts, and gatekeeper models for final checks.

5. Testing, Safety, and Observability

Unit tests, integration tests, logging, cost monitoring, and safety guardrails ensure agents act predictably and securely.

7. Infrastructure

Dockerized agents, lightweight orchestrators, and MCP setups make deployments scalable and reproducible, while CI/CD ensures reliability.

Hands-On Projects

The course included 8 real-world projects that put theory into practice:

• Career Digital Twin: a personal agent representing me to potential employers.
• SDR Agent: automated professional sales emails.
• Deep Research: a multi-agent research team for any topic.
• Stock Picker Agent: automated investment recommendations.
• 4-Agent Engineering Team: build, test, and deploy software apps collaboratively.
• OpenAI Operator Agent: a browser sidekick using LangGraph.
• Agent Creator: agents that create and deploy other agents.
• Capstone Trading Floor: 4 autonomous trading agents leveraging 44 tools across 6 MCP servers.

Final Thoughts

This course showed me that Agentic AI is more than prompt engineering—it’s software engineering at scale. It’s about designing, testing, and orchestrating systems that think, act, and collaborate autonomously. With these skills, I can now build agentic workflows that are robust, scalable, and ready for real-world applications.