Accelerating Proof with Agentic Coding

By Ben Greenberg | RealmOne Foundry

When building solutions for mission use cases, a working prototype often moves the conversation faster than a requirements document. But rapid prototyping only delivers value if it’s actually rapid. Traditional development friction (setup, scaffolding, ramping up on unfamiliar languages or libraries) can consume days before producing anything worth showing. At RealmOne’s Foundry Research and Development Lab, agentic coding tools are becoming a critical lever for collapsing that friction, allowing engineers to focus primarily on the architecture and the outcome.

AI-assisted coding has evolved quickly. The first wave involved copy/pasting into ChatGPT. Next came autocomplete tools like GitHub Copilot, which live in your IDE and suggest the next few lines as you type. Now, we have agentic coding tools that live in your CLI. Tools like Claude Code can navigate your whole codebase on the fly, working for an hour or more toward a singular goal: compiling, writing tests, and actually running your program in the process.

In rapid prototyping, a disproportionate amount of time goes to setup, scaffolding, and ramping up on unfamiliar tools, because every prototype is a fresh start, often with a different tech stack than the last. AI in general helps through things like syntax assistance, boilerplate generation, and answering questions about libraries you’ve never used. That’s been true for a while. What agentic coding tools add is the autonomous loop and the ability to act. With a chat interface, you’re still pasting errors back and forth, babysitting every iteration. With an agent, you describe the goal and your implementation constraints, and it grinds through compiler errors and failing tests until the code actually runs.

To understand how that loop works, it helps to distinguish agents from the standard LLM chatbots most people are familiar with. A standard LLM is a passive predictor: you give it text, it predicts the next word, and the interaction ends. A coding agent wraps that capability in a control loop, giving it the ability to reason through a problem, take action, and iterate toward a goal. When our engineers task an agent with a complex objective, it enters a recursive cycle:

Plan: The agent analyzes the request, scans the current file structure to understand the context, and formulates a step-by-step plan.

Action: It executes terminal commands to create files, install dependencies, or edit code blocks.

Observation: This is a critical differentiator. The agent reads the output of its actions. If a compiler throws an error or a test fails, the agent “sees” the failure, updates its plan, and attempts a fix without human intervention.

This ability to self-correct helps us tremendously with the “blank page” phase of a prototype, reducing the time it takes us to go from zero to a working skeleton.

We’ve found it helpful to guide the agent with a configuration file (CLAUDE.md, agents.md, or similar) that encodes our preferences and project-specific rules. These files can specify how we like to structure code, how we prefer to handle errors, what libraries we favor, and what patterns to avoid. In longer sessions, this keeps the agent aligned with how we truly work rather than defaulting to generic solutions.

Managing context matters more than you’d expect with these tools. LLMs have finite context windows, so agents compress as they go: summarizing progress, tracking files touched, and noting what’s been tried. Some create their own scratchpads (a TODO.md or log of attempted fixes) and work against those systematically. A well-defined goal helps here: the agent can track progress toward a clear target even as earlier details get summarized away.

That brings us to prompting. We’ve found that the tasks that work best follow a pattern: clear goal, tedious execution.

Ideal tasks include: “Add input validation to all endpoints in /routes.” OR “Fix this failing test and make sure the rest of the suite still passes.” OR “Debug this error and keep going if fixing it surfaces more errors downstream.”

Less suitable tasks include: “Build the authentication system.” OR “Make this code better.”

The latter tasks require judgment that still needs human intervention. When we match tasks to the pattern, the agent grinds through the work without losing context or needing a break.

Agentic coding tools are powerful because they can take actions, not just suggest text. That also means they can end up interacting with other layers of your development stack, like IDEs, extensions, build tools, linters, test runners, terminals, and CI hooks in ways that weren’t originally designed with an autonomous “tool-using” actor in mind. When you combine untrusted inputs (repo content, PRs, issues, docs) with an agent that can read/write files and run commands, seemingly harmless features can compose into unexpected attack paths (e.g., prompt injection → tool invocation → unintended execution or data exposure). Risks can be reduced by isolating agent runs, minimizing permissions, restricting execution, keeping secrets out of reach, and reviewing agent output like any external contribution.

We know that not everyone working on mission can spin up a commercial AI coding tool today. Tooling restrictions are real, and caution is warranted. But this capability isn’t locked to any single vendor. Open-source, model-agnostic frameworks exist and are maturing quickly. They can run against local or self-hosted models, which may have a clearer path through approval processes. The underlying patterns (tool use, iterative execution, and context management) transfer regardless of which model is driving them. RealmOne’s Foundry Team is testing these workflows now, so the patterns are proven and ready when secure infrastructure catches up.

Agentic coding is not about replacing engineers. It is about removing friction so they can spend more time solving the problems that matter most to mission outcomes. At RealmOne Foundry, we are actively exploring how these tools can accelerate proof, reduce iteration cycles, and unlock new approaches to secure, mission-focused development. If you want to learn more about how the Foundry team is applying advanced R&D practices to real-world challenges, visit our Foundry page at RealmOne.com and see how we are turning emerging ideas into operational capability.