MissionControl

Inspiration

We wanted to move beyond AI assistants that simply answer questions and build an agent that can remember, act, and verify. MissionControl is designed to preserve project context across sessions, learn from previous decisions, execute multi-step tasks through tools, and verify outcomes rather than merely generating recommendations.

What it does

MissionControl maintains persistent memory of project details, preferences, decisions, and constraints. It uses this context to break goals into manageable tasks, coordinate tool-based actions, track progress, and report completed work alongside outstanding issues. Its goal is to turn AI conversations into structured, actionable workflows that continue across sessions.

How we built it

We built MissionControl around a React and TypeScript frontend, a Python backend, and PostgreSQL for persistent data and workflow state. An AI planning layer supports reasoning and task planning, while a self-hosted Model Context Protocol (MCP) server using Streamable HTTP provides a standardized interface for tool integration. We are also exploring Amazon Web Services, including Amazon Bedrock, to support AI-powered planning.

Challenges we ran into

One of our main challenges was designing memory retrieval that preserves relevant context without relying on outdated information. We also faced challenges in maintaining consistency across sessions, coordinating multi-step tool execution, handling failures, and distinguishing between an attempted action and a verified result. These challenges reinforced the importance of reliability and observability in agentic systems.

Accomplishments that we're proud of

We have brought together the core components of a persistent, tool-enabled AI agent: a dedicated frontend, Python API backend, PostgreSQL integration, and an MCP server. We have also deployed the application components, establishing the foundation for testing an end-to-end workflow that combines contextual memory, task execution, and outcome verification.

What we learned

Building a useful AI agent requires more than a capable language model. Persistent memory, relevant context retrieval, structured planning, reliable tool integration, failure handling, and explicit outcome verification are all essential. We learned that an agent's value depends not only on what it can generate, but also on how reliably it can carry out and validate actions.

What's next for MissionControl

Our next steps are to validate the complete workflow, improve memory retrieval, develop a context graph connecting facts, decisions, and tasks, expand tool integrations, and strengthen execution verification. We aim to make MissionControl a dependable project assistant that maintains continuity across sessions and turns complex goals into traceable, actionable results.

Built With

• ai/llm
• alexa+
• amazon-web-services
• bedrock
• mcp
• persistent-memory-database
• python
• react
• streamable-http