Define agentic development, where it creates leverage, and when not to use it
Agentic development is building software with AI agents as active collaborators — systems that can pursue goals over time by planning, using tools, tracking state, and learning from feedback.
This is not the same as “prompting,” “automation,” or “RAG.” Those are techniques. Agentic development is a systems discipline: you define roles, contracts, tools, memory, evaluation, and guardrails so agents behave reliably under real constraints.
Agentic systems shine when the work is:
If the work is deterministic and repeatable, automation is often a better fit.
A practical agent loop looks like:
Agentic development is designing this loop so it’s safe, observable, and predictable.
Q: What makes a system “agentic” rather than a prompt or automation?
<details> <summary>💡 Reveal Answer</summary>An agentic system is goal-directed and iterative. It can plan, take actions via tools, maintain state over time, and adapt based on feedback. A single prompt or fixed workflow does not.
</details>Full access
Unlock all 12 lessons, templates, and resources for Agentic Development. $149 AUD.
Pick three tasks in your current workflow. For each, answer:
If you answer “yes” to at least two, it’s a candidate for an agentic approach. If not, start with automation.
Scenario: You need a weekly sales report. The data lives in one spreadsheet, and the report format never changes.
Should this be an agent?
No. This is deterministic and repeatable. A simple automation (script or scheduled report) is faster, cheaper, and more reliable. Save agents for work that needs planning, iteration, and judgment.
| Idea | Remember This |
|---|---|
| Definition | Agentic systems pursue goals over time with tools and feedback |
| Not agentic | Prompting and deterministic automation are different categories |
| Leverage | Agents shine in multi-step, uncertain, tool-heavy work |
| Core loop | Explore → Plan → Execute → Review |
Next: Agent Runtime Basics: Models, Loops, and State