Good Practices Building Agentic AI Projects

Creating agentic AI projects - systems that can reason, act, and learn autonomously - requires practical and thoughtful engineering. The following ten principles can form the foundation for project success.

1. Start With the Problem

Instead of jumping to solutions, begin by thoroughly understanding the real-world problem you are tackling. This anchors your project in genuine business value.

2. Define Your Metrics

Establish clear metrics to measure success from day one. Whether it’s ROI, accuracy, speed, or user adoption, metrics help guide development and decision-making.

3. Workflow First, Not Autonomy

Agentic AI is alluring, but workflows create reliability. Build stable, rule-based processes before introducing higher-level autonomy or dynamic behaviors.

4. Bottom Up Thinking

Innovate from the ground up. Assemble robust, incremental workflows and smart automation before layering on global controls or orchestrations.

5. Simplicity Is Key

Start with the simplest viable system. Add features, complexity, and autonomy only when the basics are proven and functioning smoothly.

6. Use Large Models Initially, Then Pare Down

Begin prototyping with advanced frontier models for capability and flexibility. Once requirements are clear, switch to smaller models that run faster and cheaper.

7. Context Over Memory

Make agents deeply context-aware. Design them to leverage recent information and the environment, rather than relying solely on memory or logs.

8. Prompt-Based Problem Solving

Most AI challenges can be solved by crafting better prompts. These guide reasoning, improve outputs, and reduce error.

9. Audit the Traces

Regularly inspect decision traces and action logs. Understand not just what the agent did, but why, to continuously improve robustness.

10. Be a Scientist

There are no shortcuts to successful agentic AI. Use an iterative, scientific approach: experiment, observe, refine, and repeat.

Agentic AI is transformative, but grounded, stepwise engineering wins every time. By focusing on real problems, maintaining simplicity, prioritizing workflow reliability, and using metrics to steer development, your AI project stands the best chance for lasting impact and scalable results.