Beyond Autocomplete: How 'Agentic Coding' Is Turning AI into a Developer's Partner

Introduction

The conversation around AI in software development is evolving quickly, moving well beyond simple autocomplete suggestions. According to Jasper Baetens from Angler Zero, the industry is shifting from a model where developers write every line of code to one where they supervise AI agents that handle the heavy lifting. This isn't about replacing developers, but creating a new and more powerful collaboration between human and machine.

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Finding the Sweet Spot Between Control and Speed

Jasper maps the current state of AI in development across a spectrum. At one end sits the traditional “no AI” approach, which offers maximum control but operates at a slower pace. At the opposite extreme is what some call “vibe coding,” where developers sacrifice control for raw speed, often resulting in lower-quality code. The most effective strategy, he argues, is a middle ground called “agentic coding,” where entire tasks are delegated to an autonomous AI executor.

Here, he makes a key distinction between “supervised” and “fully agentic” coding. The idea of a fully autonomous agent completing an entire sprint overnight sounds promising, but Jasper suggests it currently feels “a bit gimmicky” and would require a massive investment in automated quality assurance. Instead, he advocates for supervised agentic coding. This approach is much like assigning a task to a junior developer: you break it down into smaller pieces, review the subtasks, and stay in control of the overall process. It finds a practical balance between speed and quality, but it requires a different mindset from developers.

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The Art of Talking to Your AI Agent

So, how do you manage an AI agent effectively? Jasper highlights a critical skill he calls context engineering. He defines it as “filling the context window with just the right information for the next step.” The context window is essentially the AI’s short-term memory. If you overload it with irrelevant data, its performance can drop off a cliff. Jasper shared a rule of thumb he's observed: quality can decrease significantly once more than 40% of the available “tokens” (the AI’s memory units) are used. To prevent this, he recommends using simple slash commands like /clear to start with a clean slate for each new user story, which avoids polluting the context.

This principle extends to external tools. Jasper pointed out that older versions of some tools would load all their resources upfront, consuming a huge number of tokens. A recent update allows them to load dynamically, making them far more efficient. It is this type of technical awareness that separates a productive AI-assisted workflow from a frustrating one.

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A New Workflow: Research, Plan, Implement

To put these ideas into practice, Jasper’s team uses a straightforward workflow: RPI (Research, Plan, Implement). Before any code gets written, the developer prompts the agent to perform two steps, each in a clean context using separate “sub-agents.” First, a ‘research’ sub-agent analyzes the existing codebase to understand its patterns and structure. Second, a ‘planning’ sub-agent creates a detailed implementation plan.

The developer’s most important job is to review this plan before allowing the agent to proceed. As Jasper puts it, this human checkpoint is vital for preventing the AI from building the wrong feature, even if it builds it perfectly. It's much easier to correct a flawed plan than it is to refactor flawed code.

In a live demo, Jasper had the agent add a call-logging feature to a CRM. The agent used predefined ‘skills,’ such as style guides or data-fetching rules, to ensure the new code was consistent with the project’s standards. This process elevates the developer’s role from a line-by-line coder to a supervisor and strategist who guides the AI to the desired outcome.

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Conclusion

Jasper’s insights demystify agentic coding, showing that you can't just throw prompts at an AI and expect great results.

Success demands a disciplined, structured approach. By understanding the spectrum of AI assistance, embracing the new role of a supervisor, and mastering context engineering with frameworks like RPI, developers can maintain control while gaining a significant boost in speed.

The job is shifting from being a pure builder to being an architect who guides AI partners to create better software, faster.

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Ready to leverage AI in your development process? Contact us to explore how agentic coding can help you.