AgilityFeat worked with an AI-native enterprise software startup, Compounds.dev, to implement and validate a Minimum Viable Product (MVP) for their software delivery platform. The platform combines spec-driven development, codebase analysis, and AI-assisted engineering to help teams build and ship faster with greater structure, context, and control.
“AgilityFeat helped us move quickly on this technically complex project. Their team brought senior-level software development expertise, applying strong practices in modern software architecture, AI application development, LLM workflows, and RAG integration. It was exactly the kind of partnership we needed to move the product forward.” – Matt Everard, COO, Compounds.dev
The Vision: Bringing structure and control to AI-assisted development
The vision of the Compounds.dev team is to unify fragmented delivery workflows across code, systems, knowledge, security, and governance by creating an end-to-end SDLC platform where AI agents can participate in engineering work with full context, while teams retain visibility, control, and compliance awareness.
Compounds.dev is tackling a growing enterprise problem: AI can dramatically increase individual developer output, but that output does not automatically translate into better team or company-level delivery. More pull requests, larger changes, and faster code generation can also create longer review cycles, more variability, and new quality risks when teams lack shared standards, clear specifications, and integrated validation.
Compounds.dev is designed to give AI-assisted engineering a clearer operating model: defined specs, architectural context, implementation constraints, security expectations, and human-in-the-loop validation. Instead of letting coding agents start from incomplete context and produce work that teams must manually interpret, review, and correct, Compounds helps create a shared contract between the developer, the agent, and the engineering organization.
The platform connects IDEs like VS Code and Cursor, as well as agentic tools like Claude Code and OpenAI Codex, to the Compounds Cloud through MCP, giving developers access to deeper codebase analysis and a best-practices database. Rather than leaving architectural and implementation decisions entirely up to a coding agent, Compounds guides developers through human-in-the-loop decisions with regard to both structure and context.
A technically complex, bleeding-edge build
This was a technically ambitious project involving LLM workflows and RAG integration to power deep codebase analysis and surface relevant best-practice guidance at the right moments in the development workflow. Because the client’s product was so closely tied to software engineering best practices, technical decisions throughout the PoC carried extra weight. Every architectural choice needed to align with the long-term vision of the platform and reflect the same quality standards the product itself was designed to promote.
What AgilityFeat helped validate
The MVP focused on proving core functionality for an AI-native development platform, including:
- Spec-driven workflows for AI-assisted software delivery
- Codebase analysis to identify patterns, dependencies, and implementation context
- Task decomposition into specs, tasks, and subtasks
- MCP-based integration with tools like Cursor and Claude Code
- Human-in-the-loop clarification for key architectural and product decisions
- Security- and compliance-aware implementation guidance
Project constraints
The MVP was scoped to prove the main functionality without over-engineering, demonstrating acceptable performance and responsiveness while building on clean, maintainable, and scalable software practices. The goal was to avoid unnecessary complexity while still leaving a solid foundation for future development.
Project results
The MVP demonstrated the core workflow end-to-end, validated the technical feasibility of the product direction, and showed that agentic software delivery can be made more structured and practical for real engineering teams. It also reinforced that AI-assisted engineering works best when supported by clearer specifications, stronger security choices, more complete implementation planning, and disciplined architecture decisions.
Ready to build your AI-native product idea?
AgilityFeat helps startups and product teams build PoCs, MVPs, and production applications for agentic software delivery, AI-native platforms, and enterprise development workflows. If you’re exploring a new product direction and need a technical partner to help prove it, we can help in a variety of ways:
- Builder Pods – Small, senior, AI-powered nearshore teams that build fast. Reserve a dedicated pod for your next product launch, skunkworks project, or proof of concept.
- Full product development – Vision meets execution through cost-effective turnkey engagements with our full-service in‑house nearshore team. Deep expertise in integration work, including conversational agents and AI technology.
- Nearshore Staff Augmentation – Augment your existing team with experienced AI engineers.
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