Overview
An enterprise platform for computational science needed an agentic core: a system where scientists describe what they want in plain language, and specialized AI agents plan and execute the computational work — molecular dynamics simulations, quantum chemistry calculations, cheminformatics analyses — on distributed GPU compute. As lead consultants, we built that agentic layer and hardened the platform for enterprise deployment.
The Engagement
Scientific workloads are a hard fit for off-the-shelf agent frameworks. Jobs run for hours on GPU clusters, a single conversation can span multiple scientific domains, and enterprise customers demand strict identity, isolation, and auditability guarantees. The engagement covered both sides of that problem: making the agents genuinely capable, and making the platform trustworthy enough for enterprise procurement.
What We Delivered
The agentic layer
We built a multi-agent orchestration core on LangGraph, coordinating specialized scientific agents for molecular dynamics, quantum chemistry, and cheminformatics:
- Intent routing — user requests are classified and dispatched to the right specialist agent, so a solvation-energy question and a similarity-search question take entirely different execution paths
- Experiential memory — agents accumulate context from prior runs, improving how they plan and parameterize subsequent work
- LLM failover — long-running workflows survive provider outages by failing over across LLM backends instead of dying mid-run
- Distributed GPU execution — agent-planned jobs are dispatched to distributed GPU compute and tracked through completion, keeping multi-hour scientific workflows resilient end to end
Security
Enterprise deployment demanded defense in depth:
- Enterprise SSO via Keycloak with Microsoft Entra ID federation, plus admin-managed access allowlists
- Guardrail middleware validating and redacting both inputs and outputs around every LLM call
- Isolated per-conversation workspaces, so one conversation's files and artifacts can never leak into another
- Secure service-to-service token exchange between platform components
The enterprise platform
Beyond the agents themselves, we delivered the capabilities that make the product sellable to enterprises:
- Multi-tenant realm architecture — each customer organization gets isolated identity and data boundaries
- BYOK (bring-your-own-key) LLM provider key management — customers use their own model provider accounts and keys
- Agent studio and marketplace — teams can build, publish, and share custom agents on the platform
- Full observability — OpenTelemetry tracing across the agent graph, task queue, and services, so every workflow is debuggable in production
Stack
Python · LangGraph/LangChain · Next.js/React · Keycloak · Celery/Redis · PostgreSQL · Docker/Kubernetes
What This Demonstrates
Agent demos are easy; agent platforms are not. This engagement is what it takes to move from a working multi-agent prototype to something an enterprise will actually deploy: identity federation, tenancy, key management, guardrails, and observability — built around an agentic core that does real scientific work.
Building an agentic system that needs to survive enterprise scrutiny? Talk to us.