Agentic AI Development

Agentic AI development means building systems that figure out the steps themselves to reach a goal you give them. Instead of writing a rigid, step-by-step workflow (a fixed automation that does exactly what you tell it), you describe the outcome you want, and the AI decides how to get there on its own. As one expert explains, “You basically give them an outcome, not just a flow, and they figure out the steps by themselves.”

This matters because it changes your job. The key skill shifts from writing code to designing what agents should actually do and where they should be proactive. You are no longer supervising every action; instead, you provide guidance and direction and step out of the loop as much as possible. The most effective approach uses an “agentic harness” (a structural framework that organizes how agents work and communicate) to manage the system’s flexibility.

However, agentic development isn’t always the right choice. Deterministic workflows (predictable, rule-based automations) beat AI agents nine times out of ten for straightforward tasks. Adding an agentic skill when a simple, reliable automation would work is overkill. The art is knowing when to hand the AI an outcome and let it figure out the process versus when to map out every step yourself.

To build your first agentic AI system, begin with a clear plan. Before writing any code, define the problem, the tools you'll use, and the final outcome. As one expert explains, "If you can't explain clearly what you want, then how could you expect a human or an AI agent to actually build that?" Start in plan mode to create a detailed description of the workflow you want your agent to execute.

Once your plan is ready, open Claude Code — a tool that lets you build AI agents. The process follows three steps. First, write clear instructions for the agent. Second, give it access to the tools it needs, such as file reads or terminal commands. Third, teach the agent by showing it examples of the work you want it to do. For example, you might instruct Claude: "I want to build a workflow where we have an AI agent" and then let it generate the code.

For more advanced systems, combine tools. Hermes acts as an "agentic OS (operating system for running AI agents)" that can run 24/7. When paired with Claude Code, Hermes gains visual intelligence, letting it improve itself based on how you use both tools. This creates a universal AI system that learns from your interactions. Begin with Hermes to get a working agent quickly, then refine by blending features from different platforms. Remember: start with workflow fundamentals before jumping to agents.

When building agentic AI systems (AI that can take actions on its own), beginners often fall into a few key mistakes. The first is thinking you build agents themselves. Instead, "you build the information that they read" — the agent is just whatever AI you point at your information. The real work is creating a clear hierarchy of instructions and context for the AI to follow. Claude Code, Hermes, or any other tool becomes your agent by reading that structure.

A second common pitfall is using one AI model for everything. The best practice is to "match the right intelligence to the right task." You can run both Claude Code and Hermes simultaneously, saving up to 50% by using cheaper models for simple tasks and reserving powerful models for complex reasoning. Open AI even shipped an official plugin inside Claude Code for this purpose.

Third, many beginners fail to review generated code. AI still makes mistakes constantly — you should never trust output without checking it. Treat the AI as a powerful assistant, not an infallible expert.

To start well, follow a three-step system: (1) write clear instructions, (2) give the agent access to tools, and (3) teach your agent by providing examples. Master one agentic coding tool first before expanding. Becoming "extremely dangerous" at one tool teaches you the highest-leverage patterns you can reuse everywhere.