Clears

Overview

Clears is an agentic software delivery platform for teams that want AI agents to execute more than isolated coding prompts. The product is positioned around moving from backlog items to reviewed pull requests, with agents coordinating requirements, implementation, testing, code review, and delivery context.

That puts Clears in a different lane from a standalone AI code generator. Coding assistants help a developer work inside an editor. Clears is trying to manage the delivery loop around that work: which story is being executed, which context the agent needs, which systems it touches, and how humans approve the result.

The official site emphasizes agentic workflows, a context layer, and MCP-based orchestration. That makes it most relevant for engineering organizations that already trust AI for code tasks and now need a governed way to run agent work across tickets, repositories, pull requests, and reviews.

Key Features

• Backlog-to-PR execution - Clears is designed to move software work from a backlog item into implementation and reviewed pull requests.
• Agentic workflows - Teams can coordinate fleets of agents across software delivery systems instead of running one-off prompts.
• Context layer - Clears keeps memory and context across agent runs, reducing the need to rebuild delivery context from scratch.
• MCP orchestration - The official homepage describes work from an MCP terminal where agents, tickets, and pull requests can be coordinated.
• Human approval flow - Clears is positioned around reviewed output, not unreviewed autonomous merges.
• SDLC focus - Requirements, implementation, review, and delivery evidence are part of the product positioning.

How to Get Started

Start with a class of work that is frequent, bounded, and well specified: bug fixes, small product stories, internal tooling changes, test coverage tasks, or refactors with clear acceptance criteria. Clears will be easier to evaluate when the input ticket has concrete context and the output can be inspected as a pull request.

Before adding sensitive repositories, define the operating rules: which repos agents may access, what they can run, who reviews pull requests, what test evidence is required, and when a human must take over. Those rules matter more than the prompt itself.

For teams comparing AI agent platforms, Clears should be judged on delivery traceability. The question is not just "can it write code?" It is whether engineers can understand what the agent did, why it did it, and whether the resulting pull request is safe to review.

Pricing & Plans

Clears lists free trial access and public paid entry pricing, with enterprise terms likely depending on team size, repository access, agent usage, and governance needs.

Engineering leaders should evaluate cost against delivery throughput and review quality, not only seat price. Agentic delivery can create review work if tasks are too vague or output is not well tested.

Best For

• Engineering teams piloting autonomous work on bounded backlog items
• Developer productivity teams standardizing agent workflows across repositories
• Startups that want more throughput from small engineering teams without removing review
• Organizations already using MCP-aware tooling and coding agents
• Teams that need delivery memory and evidence across repeated agent runs

Clears is worth evaluating when AI coding is no longer the hard part. If the hard part is coordinating work, preserving context, and turning agent output into reviewed PRs, Clears is in the right problem space.

FAQ