Underpass AI
Memory and execution infrastructure for reliable AI agents.
Underpass AI helps agents recover context, coordinate specialist work, execute through governed tools, and leave auditable evidence behind.
We do not build foundation models. We build the operational substrate around them: navigable memory, event-driven coordination, governed execution, evidence, policy, and observability.
Problem
Agents are getting better at work, but weak infrastructure makes them hard to trust.
Modern agents can read code, call tools, run tests, and operate across complex workflows. But most agent systems still lose context between runs, repeat failed attempts, hide state inside frameworks, and produce weak audit trails.
- They lose relevant context between executions.
- They repeat failed attempts because past process memory is not navigable.
- They cannot always explain what evidence supported a decision.
- They execute tools without enough governance or inspection.
- They are difficult to observe, debug, and audit in production-like systems.
Platform model
Three infrastructure planes around the model.
Underpass separates memory, coordination, and execution so each part can be inspected, tested, and evolved independently.
Agents should not start every task from zero. They should recover scoped memory, understand previous attempts, coordinate the next step, execute through controlled runtimes, and write evidence back into the system.
- Domain event
- Specialist agents
- Underpass KMP restores scoped memory
- Choreographer coordinates deliberation and work
- Runtime governs tool execution
- Evidence is recorded
- Memory improves for the next event
Components
Public infrastructure components.
KMP and Runtime are the main public technical assets. Choreographer is public and in active development.
- Public memory plane
Underpass KMP
Kernel Memory Protocol for temporal, multidimensional, auditable AI agent memory.
- Scoped memory with about and dimensions.
- Temporal traversal: goto, near, rewind, forward, trace, inspect.
- Evidence-backed deterministic retrieval.
- Explicit relations and provenance.
- Typed gRPC API plus MCP adapter over the same semantics.
- Kubernetes/Helm deployment path.
- Adapter-based persistence for graph, key-value, and event roles.
- Public execution plane
Underpass Runtime
Governed execution plane for tool-driven AI agents.
- Isolated workspaces.
- Governed tool execution.
- Policy checks before execution.
- Telemetry and evidence trails.
- Adaptive tool recommendations.
- mTLS- and Kubernetes-oriented deployment.
- OpenAPI/gRPC-oriented runtime surface.
- Public coordination plane in active development
Underpass Choreographer
Event-driven coordination for specialist agent councils.
- Reacts to domain events.
- Composes specialist councils.
- Runs deliberation and orchestration flows.
- Publishes outcome events.
- Domain-agnostic and provider-agnostic.
- API-first gRPC and AsyncAPI contracts.
Why now
Agentic systems are moving from demos to operational workflows.
LLMs are becoming capable enough to use tools, inspect code, run commands, and participate in real engineering workflows. That creates a new infrastructure problem: memory, governance, auditability, and observability need to become first-class parts of the system.
- Software engineering is one of the first domains where agentic workflows can produce measurable feedback.
- Tool-using agents need runtime isolation and policy checks.
- Long-running agents need memory that can be navigated, not just retrieved.
- Human operators need evidence trails, traces, and failure classification.
- GPU-backed local and distributed inference is a strategic technical direction.
Open source
Built in the open.
Underpass AI is developed around open infrastructure principles. The public GitHub organization exposes the technical direction, the core repositories, and the foundation for future community collaboration.
Underpass Choreographer
underpass-choreographer · Rust · Apache-2.0
View Underpass Choreographer repo →Founder
Founder-led technical infrastructure.
Underpass AI is created by Tirso García Ibáñez, a software architect with 15+ years of experience across software engineering, architecture, distributed systems, cloud-native platforms, and technical leadership.
The current focus is AI infrastructure for agentic systems: navigable memory, governed execution, Kubernetes-native runtime foundations, and production-oriented architecture patterns for LLM agents.
Contact
Talk to Underpass AI.
Interested in reliable agent infrastructure, technical validation, open-source collaboration, or early-stage product conversations?
- General contact@underpassai.com
- Founder tgarciai@underpassai.com
- GitHub github.com/underpass-ai