loopd

The Challenge

AI coding agents have become remarkably capable at generating code, but they still struggle with one critical problem: knowing when they are actually finished.

Most agentic systems rely on the model to judge its own output, often leading to false confidence, broken implementations, weakened tests, or incomplete features that appear successful but fail under real verification.

I wanted to build an engineering runtime that treated autonomous software development like a production engineering workflow—where planning, implementation, verification, and review are separate responsibilities rather than a single AI conversation.

The Solution

loopd is an autonomous engineering runtime that coordinates multiple AI agents through a deterministic orchestration layer.

Instead of allowing a coding model to independently complete an entire task, loopd separates planning from implementation using specialized agents while keeping all execution rules outside the language model.

A persistent planner decomposes complex engineering tasks into manageable steps, disposable developer sessions implement each step independently, and an external verification engine determines whether work actually satisfies predefined acceptance criteria before any progress is accepted.

This architecture enables long-running autonomous software development without trusting the model to evaluate its own work.

Persistent Planning Architecture

At the core of loopd is a long-lived planner session responsible for maintaining project context throughout an entire execution.

The planner:

• Breaks complex engineering work into incremental tasks

• Generates implementation instructions for developers

• Reviews completed work

• Decides whether to accept, reject, replan, or terminate execution

• Maintains long-term engineering memory across runs

Unlike conventional multi-agent systems where every interaction starts with limited context, the persistent planner continuously evolves its understanding of the project throughout execution.

Disposable Developer Sessions

Rather than allowing a single AI conversation to accumulate context indefinitely, every implementation task is executed inside a fresh developer session.

Each developer is responsible only for:

• Implementing a single engineering task

• Producing clean Git diffs

• Returning structured handoff packets

• Iterating until verification succeeds

By isolating implementation from planning, loopd minimizes context drift while ensuring every coding task starts from a clean execution environment.

Deterministic Verification Engine

The defining characteristic of loopd is that AI models never determine whether their own work is complete.

Instead, verification is performed entirely outside the model using deterministic execution.

Verification can include:

• Unit tests

• Integration tests

• Build validation

• Linting

• Docker builds

• HTTP endpoint checks

• Environment validation

• Custom shell commands

Only when every verification gate succeeds can a task be accepted and committed, preventing models from approving incomplete or incorrect implementations.

Autonomous Engineering Workflow

loopd executes software development through a structured orchestration pipeline:

• Task planning and decomposition

• Budget and runtime forecasting

• Developer implementation

• External verification

• Planner review

• Step-by-step Git commits

• Final regression replay inside a pristine repository

• Delivery confidence scoring

Each accepted task becomes an isolated, reviewable Git commit, creating an auditable engineering history throughout autonomous execution.

Reliability & Engineering Safety

Several mechanisms were designed to improve reliability during long-running autonomous execution.

These include:

• Budget-aware execution forecasting

• Automatic retry and replanning strategies

• Failure analysis with actionable escalation reports

• Engineering memory across sessions

• Resumable execution after interruption

• Delivery confidence scoring based on deterministic evidence rather than model self-assessment

These safeguards allow loopd to operate autonomously while remaining transparent and reviewable.

Outcome

loopd evolved into a complete autonomous engineering runtime capable of coordinating multiple AI agents while maintaining deterministic software engineering standards.

Instead of simply generating code, the system continuously plans, verifies, reviews, commits, and measures confidence throughout the entire development lifecycle.

Highlights

• Persistent planner with long-term execution context

• Disposable developer agent architecture

• Deterministic external verification pipeline

• Multi-agent orchestration engine

• Execution forecasting and budget management

• Automated failure analysis and replanning

• Delivery confidence scoring

• Step-wise Git commit workflow

• Resumable autonomous execution

• GitHub integration for issue-driven development

What I Learned

Building loopd fundamentally changed how I think about autonomous software engineering.

I realized that building better coding agents isn't primarily about making language models smarter—it's about designing reliable systems around them. Separating planning from implementation, enforcing deterministic verification, and treating software development as an orchestrated workflow rather than a single AI interaction leads to significantly more trustworthy autonomous systems.

The project reinforced the importance of engineering guardrails, reproducible verification, and system architecture in building AI applications that can operate with increasing levels of autonomy while remaining dependable.