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Articles by Daniel Butler
Intent-Driven Development Changes the Shape of Risk
Agentic coding increases throughput in ways that are now observable in practice. Tasks that previously required sustained effort can be implemented quickly, boilerplate largely disappears, and refactors that were once deferred become cheap enough to attempt. By intent-driven development, I mean working at the level of describing outcomes and constraints while an AI agent generates the implementation.
Did that actually help? Evaluating AI coding assistants with hard numbers
You are building a Skill, an MCP server, or a custom prompt strategy that is supposed to make an AI coding assistant better at a specific job. You make a change. The next session feels smoother. The agent seems to reach for the right context at the right time. But how do you know? That question came up in two parallel problems. I was building and iterating on MCP servers to support a coding agent. New tool, new tool definition, new prompting strategy. Each change felt like an improvement.
Agentic workflow design - index and reading order
This series documents an enterprise workflow design for working with AI coding agents. It is not a prompt collection and it is not tied to a single tool. It is a structural approach based on observed failure modes in production environments and the gaps that exist in current agentic tooling. Agentic coding increases throughput. It also increases the probability of shipping something we did not intend to ship. The workflows described here are designed to close that gap.
Designing agentic workflows: supplementary commands and pressure valves
In the previous posts, we established: Designing agentic workflows: where agents fail and where we fail Designing agentic workflows: a practical example Designing agentic workflows: the core loop The core execution loop: Define gates Plan one bounded task Implement exactly one task Repeat until all gates pass Run cleanup That loop is the default path. Most issues go straight into it. The supplementary commands do not replace the loop.
Designing agentic workflows: the core loop
The previous posts laid out the failure modes and the initial structure: Designing agentic workflows: where agents fail and where we fail https://dev.to/danielbutlerirl/designing-agentic-workflows-where-agents-fail-and-where-we-fail-4a95 Designing agentic workflows: a practical example https://dev.to/danielbutlerirl/designing-agentic-workflows-a-practical-example-291j Those posts described where agents fail, where we fail, and what a constrained workflow looks like in principle.
Designing agentic workflows: a practical example
The previous post focused on failure modes: where agents fail, and where we fail when reviewing their output. This follow-up shifts from diagnosis to design. What follows is a sample workflow that is explicitly structured to counter those failure modes. It is not the only way to do this, and it is not meant to be permanent. It is one workable design intended to turn known risks into explicit constraints and force verification back into the loop.
Designing agentic workflows: where agents fail and where we fail
Agentic coding increases throughput. It also increases the probability that we ship something we didn't mean to ship. Both the agent and the review process tend to optimise for visible success signals: green tests, plausible diffs, confident summaries, "done".
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