M31A's agentic workflow aims to ship code, not just suggest it

A new terminal-native AI agent, M31A, is built around a six-phase workflow that ends every run with a verified git commit, positioning itself as an autonomous executor, not a code completion tool.

THE ANSWER UP FRONT

M31A is for developers who live in the terminal and want an AI agent that owns the entire lifecycle of a task, from planning to a verified commit. Its rigid, six-phase workflow is its core feature. You should skip it if you are deeply integrated into a GUI-based IDE like VS Code or JetBrains, or if you prefer simple, in-line code suggestions over a stateful, autonomous agent. The bottom line: M31A presents an opinionated vision for agentic development that trades the convenience of IDE integration for a structured, verifiable, and terminal-native workflow.

METHODOLOGY

This is a v0 review based entirely on the founder's technical blog post, published on dev.to, which details the project's architecture and philosophy. Independent benchmarks are pending.

• Tool: M31A (M31 Autonomous)
• Version: Not specified in the source document.
• Date Observed: June 16, 2026
• Source Signal: "Building M31A: A Terminal-Native AI Coding Agent That Ships, Not Just Suggests" at https://dev.to/eshanized/building-m31a-a-terminal-native-ai-coding-agent-that-ships-not-just-suggests-2p0m

This review covers the tool's stated architecture, its core six-phase workflow, and its positioning against other AI coding assistants, all based on the author's claims. What is not covered is any hands-on testing, performance on real-world coding tasks, the quality of the generated plans, or the robustness of its verification and error-handling loops. All functionality described is as claimed by the developer.

WHAT IT DOES

An autonomous agent, not an assistant

The central premise of M31A is to move beyond the typical AI assistant model of suggesting code snippets. The author positions it as an autonomous agent that manages a task from start to finish. Instead of a user copying, pasting, and manually testing code, M31A is designed to handle the entire sequence, culminating in a git commit. The project is a single static binary written in Go, runs in any POSIX shell, and has a stated policy of zero telemetry.

The six-phase workflow engine

The core of M31A is a structured, sequential workflow. Every task proceeds through six distinct phases:

1. Initialize: The agent inspects the project environment, detects the project type (Go, Python, etc.), and sets up a .m31a/ directory for planning and state management.
2. Discuss: It asks the user clarifying questions to fully scope the task before proceeding.
3. Plan: It generates a structured implementation plan, breaking the task into discrete steps.
4. Execute: It carries out the plan, performing actions like reading and writing files.
5. Verify: It runs checks, such as tests or syntax validation, to confirm the changes work as intended.
6. Ship: Once verified, it commits the changes to git and records its process in a "learning ledger" for future reference.

A layered, terminal-native architecture

M31A is built on a six-layer architecture designed for separation of concerns. A TUI layer, built with Bubble Tea, handles all user interaction. Below it, the Workflow Engine orchestrates the six phases. This engine utilizes three distinct modules: Providers (for LLM APIs like OpenRouter), Tools (for filesystem operations like bash, grep, FileWrite), and Domain Packages (for internal logic like session management and rollback). The foundation is an Infrastructure Layer that handles git, configuration, and file utilities. This design ensures that the UI is decoupled from the core logic and the logic is decoupled from the underlying tools.

WHAT'S INTERESTING / WHAT'S NOT

The most interesting aspect of M31A is its opinionated commitment to a complete, verifiable workflow. Ending every successful run with a git commit is a powerful statement. It forces a level of accountability and traceability that is absent from most copy-paste AI suggestions. This structure directly addresses a common pain point: AI-generated code that is syntactically correct but functionally broken, creating more debugging work for the developer. The terminal-native approach, using Go and Bubble Tea, will strongly appeal to CLI power users.

What's not clear is how well this works in practice. The blog post is an architectural overview, not a performance report. The effectiveness of the entire system hinges on the quality of the Plan and Verify phases. The author doesn't specify what "Verify" entails. Does it just run the existing test suite, or does it attempt to write new tests? How does it handle complex, multi-file changes or refactoring that touches many parts of a codebase? The project's value is entirely dependent on the execution quality of its agentic loop, and the source provides no data or examples to assess it.

PRICING

The source document describes M31A as an open-source project. No pricing or commercial tiers are mentioned. (Pricing snapshot: June 16, 2026).

VERDICT

M31A is a compelling proposal for what a developer-focused AI agent should be: an executor, not just a suggester. For developers who work primarily in the terminal and value structured, repeatable, and verifiable results, its six-phase model is a promising alternative to IDE-based assistants. However, this is a v0 assessment based solely on the author's architectural claims. The project's true utility depends entirely on the quality of its planning and the robustness of its verification stage, neither of which can be assessed from the source material. It's a project to watch, especially for CLI purists, but it remains a concept until its real-world effectiveness is demonstrated.

WHAT WE'D TEST NEXT

For a v1 review, we would need to perform hands-on testing. The first priority would be to run M31A against a small, self-contained coding task, such as adding a new endpoint to an existing web service. We would evaluate the quality of the generated plan, the correctness of the executed code, and the thoroughness of the verification step. A key test would be to see how it handles failures, for example, if the generated code fails the project's test suite. Finally, we would compare its process and output against other terminal-based agents like Aider on the same standardized task.

The investor read

M31A signals a maturing of the AI developer tool market, moving from suggestion-based 'co-pilots' to execution-based 'agents'. The focus on a verifiable workflow ending in a git commit directly targets the low signal-to-noise ratio of current AI code generation. This terminal-native approach competes with tools like Aider and differentiates from GUI-centric players like Cursor. As a solo-developer open-source project, it is not currently an investable company. To become one, it would need to demonstrate superior performance on benchmarks like SWE-bench, build a clear commercialization path (e.g., enterprise features, team collaboration), and overcome the growth limitations of its 'zero telemetry' stance. The key risk is that incumbent IDEs (VS Code, JetBrains) will build 'good enough' agentic features, making standalone tools a niche market.

Pull quote: “The bottom line: M31A presents an opinionated vision for agentic development that trades the convenience of IDE integration for a structured, verifiable, and terminal-native workflow.”