Engineering the Command Layer for AI Agents.
Agiflow bridges the gap between high-level project intent and autonomous AI execution. Our infrastructure ensures agents have the context, coordination, and standards needed to ship production-grade code.
01 // System Architecture
Persistent context provider and coordination engine for AI-assisted development.
Context Engine
Persistent storage of project requirements, architecture rules, and development history that agents can query in real-time.
Work Orchestration
Lifecycle management for features, from initial planning and grooming to execution and final triage.
State Observability
Real-time monitoring of agent actions, tool calls, and progress across all software projects in your organization.
02 // Capabilities
Core modules enabling autonomous development while enforcing architectural boundaries.
Work Units
Atomic units of work. Groups of tasks with machine-verifiable acceptance criteria.
MCP Ecosystem
Model Context Protocol servers connecting agents to your internal tools and data.
Architecture Rules
Enforced design patterns and validation rules that agents must follow when coding.
Scaffolding
Standardized code generation from templates to ensure consistency across projects.
One MCP
A proxy for progressive tool discovery and significant reduction in token usage.
Developer API
Programmatic access to Agiflow capabilities for custom CI/CD and automation flows.
Open Source Core
Our core MCP servers are open-source. Enforce architecture patterns and automate scaffolding directly in Claude Code, Cursor, or ChatGPT without a full Agiflow account.