Platform
The execution layer for work graphs in production.
Five integrated layers — from agent reasoning to enterprise observability. Not a collection of tools. A unified runtime where every layer shares state, security boundaries, and telemetry.
Agent Engine
"Work Graphs are to operations what APIs are to payments."
The execution runtime for long-running, stateful agent workflows. Built on LangGraph for graph-based orchestration with durable state, conditional branching, and automatic retry. Model-agnostic — swap GPT, Claude, or Gemini per-node without changing graph logic.
- Durable state persistence across steps
- Conditional and cyclic graph execution
- Model-agnostic: swap LLM per node
- Automatic retry with configurable backoff
Workflow Graphs
"Runs are to workflows what deploys are to code."
Model workflows as mathematical graphs — state + nodes + edges with conditional transitions. The Work Graph Compiler produces an immutable version hash, a policy manifest defining required approvals and allowed tools, and a replay plan with typed input/output schemas.
- State + nodes + edges with conditions
- Immutable compilation with version hash
- Policy manifest: approvals + tool allowlist
- Cyclic workflows with reflection patterns
Tool Abstraction
"Swap DMS for EHR. The graph stays the same."
Tools are typed contracts — the graph binds to an interface, not a vendor. Switch from CDK Global to Reynolds & Reynolds in automotive, or from Epic to Cerner in healthcare, without modifying a single node. Tool Packs are versioned and pinned per-graph.
- Typed tool contracts (DMS, EHR, CRM)
- Vendor-agnostic: swap integrations without graph changes
- Versioned Tool Packs pinned per-graph
- Scoped credentials per-tenant per-tool
MLOps + MLflow
"Version everything. Trace lineage. Detect drift."
Every model, prompt, tool pack, and policy version is tracked with full lineage via MLflow Model Registry. Monitor agent performance in real-time. Detect concept drift, feature changes, and degradation patterns before they reach production outcomes.
- MLflow Model Registry for version lineage
- Prompt + model + tool pack versioning
- Real-time performance monitoring
- Drift detection and alerting
Observability
"Every run emits events. Every action is attributable."
Structured telemetry at every layer. Trace individual tool calls with latency, token usage, and policy evaluation results. Aggregate metrics for throughput, error rates, and SLA compliance. Every state change is inspectable — search, filter, and replay.
- Structured event streams (SSE)
- Per-run trace with tool call detail
- Aggregate dashboards: latency, throughput, errors
- Full-text search across run history
Secure Java API Gateway
The gateway acts as the security perimeter for all agent actions. Deterministic gates — hardcoded security policies that no agent can bypass, regardless of its instructions. Multi-tenant isolation enforced at the request level.
- Spring Boot with zero-trust request validation
- Per-tenant credential scoping and key rotation
- Rate limiting and resource consumption controls
- OWASP API Security Top 10 alignment
- Deterministic policy gates — not model-controlled
// Java Gateway — Deterministic Policy Gate
@PreAuthorize("hasRole('TENANT_AGENT')")
@RateLimited(requests = 100, window = "1m")
public ResponseEntity<RunResult> executeRun(
@TenantScoped Long tenantId,
@Valid @RequestBody RunRequest request
) {
// 1. Validate tenant boundaries
tenantGuard.enforce(tenantId, request);
// 2. Check policy manifest
PolicyResult policy = policyEngine
.evaluate(request.getGraphVersion());
// 3. Enforce deterministic gates
if (policy.requiresApproval()) {
return Response.blocked(
policy.getGateContext()
);
}
// 4. Execute via LangGraph runtime
return graphRuntime.stream(request);
}