Agentic AI & ML Engineering

What We Build

Production agentic systems — not chatbot wrappers. Multi-agent orchestration platforms where specialized AI agents handle complex, multi-step business workflows with human oversight at critical decision points.

We don’t just build agents — we build the data platform that makes agents reliable. Agents are only as good as the data they access and the infrastructure they run on.

Our Approach

Supervisor pattern, not agent swarms. A central orchestrator routes work to domain-specific agents. Each agent has its own tools, prompts, and validated data contracts. The supervisor coordinates — it doesn’t do domain work. This is predictable, auditable, and debuggable. Agent swarms are none of those things.

Contracts at every boundary. Agents communicate through Pydantic v2 strict models. Every inter-agent message is validated at the boundary. Malformed or incomplete data is caught before it propagates. In production, this is the difference between “works” and “works reliably.”

Checkpointed state for long-running workflows. PostgreSQL checkpointing after every decision point. Workflows that span days or weeks resume from last checkpoint on failure. The state history is the audit trail — not a reconstructed log, but the primary data structure.

Capabilities

The Data Platform Underneath

• Databricks + Unity Catalog for governed data access — agents query data through the same governance layer as your analytics team
• MLflow for experiment tracking, model registry, and agent evaluation — not just ML models, but agent behavior over time
• Medallion architecture feeding agent decisions — bronze/silver/gold data pipelines ensure agents work with clean, validated, current data
• Terraform/Terragrunt for reproducible infrastructure — multi-region, multi-tenant agent deployments with tenant-level isolation

Engagement Model

1. Discovery (1-2 weeks): Understand the workflow, map decision points, identify automation candidates and human-in-the-loop gates
2. Architecture (1-2 weeks): Agent graph design, data contract definitions, tool inventory, infrastructure blueprint
3. Build (4-8 weeks): Agent implementation, integration layer, E2E test scenarios, observability setup
4. Harden (2-4 weeks): Production testing with real cases, performance tuning, monitoring and alerting
5. Operate: Ongoing agent versioning, prompt management, performance monitoring, model upgrades