Lab
The lab documents ongoing architecture work, prototype systems, and research into agentic enterprise patterns. Entries evolve as understanding deepens.
Designing a lightweight orchestration layer for coordinating specialized AI agents across enterprise workflows. Focuses on task decomposition, inter-agent communication protocols, and escalation logic. Currently prototyping with event-driven architecture on cloud-native infrastructure.
Building a context infrastructure prototype that aggregates, indexes, and surfaces organizational knowledge for AI agent consumption. Exploring vector-based retrieval combined with structured metadata graphs to enable agents to operate with executive-level situational awareness.
Researching structured logging and trace patterns for autonomous AI agents in enterprise environments. Goal is to produce an auditability framework that captures decision chains, data access events, and delegation flows in a format suitable for compliance review.
Planning a reference architecture for data validation and integrity enforcement in AI-native pipelines. Will cover schema versioning, provenance tracking, automated quality gates, and reconciliation patterns for systems where agents both produce and consume data.
Documenting deployment architectures for AI-native applications — covering containerized inference, serverless agent hosting, cost-optimized GPU scheduling, and observability stacks purpose-built for agentic workloads.