Clears
Agentic platform that takes a backlog of tickets through to reviewed pull requests autonomously, with real-time visibility and human oversight.
🔗 Visit Clears
Description
AI coding assistants are good at helping with one task at a time, but a real engineering backlog is dozens of tickets deep — someone still has to triage, assign, and track each one through to a reviewed PR. Clears tries to run that whole pipeline autonomously, with a human able to step in at any point.
Clears is an AI-powered platform for agentic software delivery: it takes a backlog of stories and carries them through analysis, scoping, risk assessment, and execution to reviewed pull requests, coordinating multiple agent sessions in parallel with shared context. It gives real-time visibility into each ticket's progress and integrates with coding agents via the Model Context Protocol (MCP). It's a commercial SaaS platform with customer testimonials from companies including BioCatch and Windward.
💬 Our review
The short version: Clears is aiming at a genuinely harder problem than most AI coding tools — orchestrating a whole backlog, not just one task — and the MCP-based extensibility is a smart architectural choice, but the lack of public pricing makes it hard to evaluate without a sales conversation.
Against using Claude Code or GitHub Copilot directly on individual tickets, Clears' pitch is orchestration: instead of a developer manually pulling the next ticket and prompting an agent, Clears runs multiple agent sessions in parallel across the backlog with shared context between them, and surfaces real-time visibility into where each ticket stands. That's a genuinely different layer of the problem than a single-task coding assistant solves. The MCP integration is a sensible bet — it means Clears can plug into whatever coding agents a team already uses rather than replacing them. The core uncertainty is maturity: pricing isn't public, and while there are named customer testimonials (BioCatch, Windward), this is still a young category without a long track record to point to. Best fit: engineering teams with a real backlog bottleneck who want to pilot agentic orchestration across multiple tickets at once, not just single-task AI assistance. Weaker fit: smaller teams or solo developers where a single coding agent per task is already sufficient and simpler to reason about.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Essai gratuit disponible ; grille tarifaire détaillée non publiée sur le site.
Pros
Exécution autonome du besoin jusqu'à la PR revue
Visibilité temps réel sur l'exécution parallèle des tickets
Intégration MCP extensible
Analyse et évaluation de risque intégrées
Clients nommés (BioCatch, Windward)
Cons
Tarification non transparente sur le site public
Nécessite de comprendre le protocole MCP pour une intégration poussée
Limites de capacité pas clairement documentées