AGENA is an agentic AI platform that autonomously generates code, reviews quality, and creates pull requests. It runs a PM → Developer → Reviewer → Finalizer pipeline powered by pixel agent technology, turning your backlog into production-ready PRs in minutes.

Agentic AI refers to AI systems that can autonomously plan, execute, and adapt to achieve complex goals. Unlike simple AI assistants, agentic AI agents work independently through multi-step workflows — analyzing tasks, generating code, reviewing quality, and shipping features without constant human guidance.

What is pixel agent technology?

Pixel agent technology is AGENA's visual orchestration layer that represents each AI agent as an interactive pixel character. It provides real-time visibility into your autonomous AI workforce — showing which agents are active, what they're working on, and the progress of each task through the pipeline.

Which platforms does AGENA integrate with?

AGENA integrates with GitHub and Azure DevOps for PR automation, Jira for task import, Slack and Microsoft Teams for ChatOps notifications, and supports OpenAI and Google Gemini as LLM providers.

Is AGENA free to use?

Yes! AGENA offers a free tier with 5 tasks per month, token usage tracking, and community support. The Pro plan ($49/month) includes unlimited tasks, priority worker throughput, team invites, and billing via Stripe or Iyzico.

Is AGENA open source?

Yes, AGENA is fully open source under the MIT license. You can self-host it or use the managed platform. The source code is available on GitHub at github.com/aozyildirim/Agena.

How does AGENA compare to GitHub Copilot?

While GitHub Copilot suggests code line by line within your IDE, AGENA is a full agentic AI platform that takes a complete task and autonomously generates production code, reviews it, and creates a pull request. AGENA handles the entire workflow from backlog to PR.

Can AGENA work with my existing CI/CD pipeline?

Yes. AGENA creates standard pull requests on GitHub or Azure DevOps. Your existing CI/CD pipeline, code review process, and branch protection rules all work as normal with AI-generated PRs.

What programming languages does AGENA support?

AGENA supports all programming languages that the underlying LLM models support, including Python, JavaScript, TypeScript, Java, Go, Rust, C#, PHP, Ruby, and more. The AI agents analyze your existing codebase to match patterns and conventions.

How secure is AGENA with my source code?

AGENA never stores your source code. Repository access is scoped via OAuth tokens, code is processed in isolated sessions, and you can self-host for complete control. All data is organization-scoped with full tenant isolation.

Glossary

AGENA Technical Glossary

Key concepts and terms in AI-powered software development

Agentic AI

AI systems that autonomously plan, execute, and iterate on tasks

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Pixel Agent

AGENA's visual AI character representing agent roles in the office view

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LLM (Large Language Model)

Foundation models like GPT-5 and Gemini that power AI agents

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RAG (Retrieval-Augmented Generation)

Enhancing LLM output with retrieved context from codebases

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Multi-Agent System

Multiple AI agents collaborating as PM, Developer, Reviewer, and Finalizer

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LangGraph

Graph-based orchestration framework for AI agent pipelines

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CrewAI

Framework for defining AI agent roles, goals, and collaboration patterns

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Vector Memory

Embedding-based storage (Qdrant) for semantic code search and context

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DORA Metrics

DevOps Research and Assessment: deployment frequency, lead time, CFR, MTTR

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Flow Builder

Visual n8n-style automation builder for custom AI workflows

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Prompt Engineering

Crafting system prompts to control AI agent behavior and output quality

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Code Review Agent

AI agent that reviews generated code for bugs, security, and best practices

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PR Automation

Automatic branch creation, code commit, and pull request generation

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ChatOps

Controlling AI agents via chat commands in Slack, Teams, or Telegram

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Multi-Tenant

Architecture where each organization has isolated data and configurations

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Sprint Refinement

AI-powered analysis of backlog items with story point suggestions

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Playbook

Organization-wide coding rules and conventions that guide AI code generation

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Token Budget

Per-task limit on LLM tokens to control cost and output length

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Blue-Green Deployment

Zero-downtime deployment strategy using two identical environments

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Webhook

HTTP callbacks for real-time event notifications between systems

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