AI Agents Planning: From Python Developer to AI Engineer

You're Not Building Agents: Learn the Fundamentals From Scratch

TLDR: Most developers think they're building AI agents when they're just running tools in loops. True agents require planning capabilities like ReAct and Plan-and-Execute patterns to handle complex scenarios and multiple tool interactions effectively.

Summary:

This article kicks off a comprehensive nine-part series that aims to transform Python developers into AI engineers capable of building production-ready AI agents. The author makes a crucial distinction that many developers miss: running tools in a loop doesn't make an agent—planning does. This is the secret sauce that separates simple workflows from true autonomous agents.

The series promises a hands-on approach, building everything from scratch to develop proper mental models. The curriculum covers workflows versus agents, context engineering, structured outputs, the five workflow patterns, planning methodologies like ReAct and Plan-and-Execute, memory systems, and multimodal data handling. This comprehensive approach suggests the authors understand that agent development requires deep foundational knowledge rather than surface-level framework usage.

What's particularly interesting is the author's admission of their own journey. They describe building ZTRON, a financial services AI agent, initially as an "agentic RAG system" that presumably lacked proper planning capabilities. This real-world experience adds credibility to their teaching approach and suggests they've learned from actual production challenges rather than theoretical exercises.

The emphasis on building from scratch is noteworthy in an ecosystem dominated by high-level frameworks. This approach should help developers understand the underlying mechanics rather than becoming overly dependent on abstraction layers. For architecture teams, this represents a valuable investment in foundational knowledge that will inform better tool selection and system design decisions. The focus on production readiness rather than proof-of-concept development addresses a critical gap in the current AI education landscape.

However, the article doesn't address some important considerations. There's no discussion of when NOT to use agents—sometimes a simple workflow is more appropriate and maintainable. The complexity overhead of planning systems isn't mentioned, nor are the debugging and observability challenges that come with autonomous systems.

Key takeaways:

• Planning capability, not tool execution, distinguishes true agents from simple workflows
• The series takes a build-from-scratch approach to develop deep understanding rather than framework dependency
• Real production experience with financial AI agents informs the teaching methodology

Tradeoffs:

• Building from scratch provides deep understanding but sacrifices initial development speed compared to using existing frameworks
• Agent planning increases system autonomy but adds complexity overhead and debugging challenges

Link: You're Not Building Agents: Learn the Fundamentals From Scratch

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