4.2B
Model and agent invocations orchestrated per month across the customer base.
Source: Clooney platform telemetry, TTM Jan 2026.
Platform / The orchestration fabric
One connected layer for every model, agent, and channel your system depends on.
Clooney Network replaces the brittle internal glue holding multi-model, multi-agent production systems together. Routing, orchestration, observability, and governance — engineered as four first-class subsystems behind a single control plane.
No procurement required — 30 minutes, your stack or ours. We come prepared with a routing and trace walkthrough against your existing model footprint.
By the numbers — published January 2026
Production-proven at the scale enterprise buyers audit first.
99.97%
Trailing-twelve-month orchestrator control-plane uptime. Published in our MSA.
Measured at the platform control plane, not customer-side model APIs.
38–62%
Inference spend reduction reported by 2024 pilot cohort after enabling cost-aware routing.
Across 14 foundation providers, deterministic (not probabilistic) routing.
11 days
Median time-to-first-production-agent for new customers in the 2024 cohort.
From signed agreement to first routed, traced workload in production.
Recognized as a Strong Performer in the Forrester Wave™: AI Orchestration Platforms, Q4 2024. Named a Cool Vendor in Agentic Infrastructure at the Gartner AI Infrastructure Summit, October 2024.
Architecture overview
Four first-class subsystems. One control plane. Nothing duct-taped.
Clooney is not a feature checklist. Each of the four subsystems below is independently addressable by API, individually observable in the platform console, and backed by its own published SLA — yet they share a single control plane so policy, identity, and trace context flow continuously between them.
The result is the layer your platform team would otherwise build, maintain, and re-platform every 18 months — already shipped, already audited, already running 4.2 billion agent invocations per month for production teams who refuse to be the bottleneck for their own AI roadmap.
- 01Routing. Deterministic, cost-aware model selection across 14 foundation providers.
- 02Orchestration. Agent handoffs, retries, and tool calls across heterogeneous runtimes.
- 03Observability. Patented trace correlation reconstructing multi-agent decision graphs.
- 04Governance. Cost ceilings, data-residency policy, and per-tenant budget enforcement.
Capability 01 — Routing
Deterministic, cost-aware model routing across 14 foundation providers.
Routing in Clooney is not a fallback chain — it is a deterministic policy layer. For every task class you define (summarization, code generation, embedding, classification, long-context reasoning), you specify the primary model, the cost ceiling per 1k tokens, the latency budget, and the eligibility constraint. Every request is then routed against that table, not against a probabilistic retry graph.
Providers supported14 foundation providers (production GA)
Decision modelDeterministic policy table, not a fallback chain
Cost ceiling enforcementPer-task, per-tenant, per-request — hard ceiling, not advisory
Pilot spend reduction38–62% on inference spend, 2024 cohort
ReplayEvery routing decision is recorded against the trace graph
The practical effect: the same prompt that today falls through an OpenAI → Anthropic → self-hosted ladder — burning margin at each hop — is now a single explicit decision with a documented cost ceiling. Your platform team stops owning a routing library and starts owning a routing policy.
Capability 02 — Orchestration
Multi-agent orchestration built for handoffs, retries, and tool calls — not for demos.
Most orchestration layers today are happy-path demos. The moment a planner agent calls a retrieval agent that fails on a tool call and needs to retry against a different model with a different prompt, your internal glue code is what's holding production together. Clooney is the system-of-record that replaces it.
Agent runtimes supported9 (LangGraph, CrewAI, AutoGen, custom)
Handoff modelExplicit, versioned, replayable graph edges
Retry semanticsPer-edge, with policy-bound model substitution
Tool-call surfaceVersioned schemas, signed payloads, per-tenant allow-lists
StateDurable execution log; every handoff is replayable from any prior step
Engineering teams report that internal agent orchestration is now the single largest line of bespoke code in their AI stack — larger than retrieval, larger than evals, larger than prompt engineering. Clooney retires that category of work, with the same deterministic guarantees that govern routing.
Capability 03 — Observability
A patented trace engine that reconstructs multi-agent decision graphs 14× faster than the closest benchmark.
Observability in an agent system is not log search. It is graph reconstruction: given a finished request, you need the exact decision tree of which agent called which, on which prompt, with which model, against which tool, with which cost — and you need it now, before your on-call engineer gives up. Clooney's trace-correlation engine is the part of the platform that does that job, and it is the part that is benchmarked against alternatives.
Trace correlation enginePatented; reconstructs multi-agent decision graphs in seconds
Benchmark14× faster than LangSmith on the SWE-Agent Trace Suite
Per-edge fidelityPrompt, response, model, tool, tokens, cost, latency
Eval surfaceReplay any prior request against any new model or prompt revision
Compliance exportSOC 2 Type II audit log; per-tenant data residency honored
The same trace graph that powers debugging powers eval — replaying a curated set of historical requests against a new model candidate, with cost and quality deltas computed automatically. Compliance reads the same graph; on-call reads the same graph; product reads the same graph. One source of truth for an agent system that, until now, had none.