By James Aspinwall, co-written by Alfred Pennyworth (my trusted AI) — March 7, 2026, 13:07
ClearML manages the entire AI lifecycle — from GPU orchestration and experiment tracking to model serving and GenAI deployment. WorkingAgents manages the entire agent lifecycle — from permissions and tool access to task scheduling and audit trails. ClearML gets AI models into production. WorkingAgents governs what those models do once they’re there.
What ClearML Does
ClearML is the leading AI infrastructure platform, used by 2,100+ customers and trusted by 300,000+ AI builders at Fortune 500 companies, enterprises, academia, and startups. NVIDIA partner. Open-source foundation with enterprise offerings.
The platform spans four product areas:
- Infrastructure Control Plane — manages and optimizes GPU resources across on-prem, cloud, and hybrid environments. Dynamic fractional GPUs boost workload capacity up to 10X. Secure multi-tenancy with quota management. Increases GPU utilization from the industry average of 20-25% to 75%+.
- AI Development Center — experiment tracking, data management, pipeline automation, and CI/CD integration. The full MLOps/LLMOps stack in one platform.
- Model-as-a-Service — one-click LLM deployment on existing infrastructure. Deploy any LLM on bare metal, VMs, or Kubernetes without operational complexity.
- GenAI App Engine — rapid GenAI deployment with streamlined tooling. Business stakeholders evaluate and iterate on GenAI applications at scale.
The numbers: 40% reduction in compute and labor costs. 200% boost in GPU utilization. Customers double and triple AI activities without additional hardware investment.
Security: SSO authentication, LDAP integration, role-based access control, isolated networks and storage per tenant.
What WorkingAgents Does
WorkingAgents is the governance and control layer between AI agents and enterprise systems. Three gateways, one control plane:
- Unified LLM Routing — control which models agents use and how they access them
- Agentic Workflow Control — define, supervise, and enforce how agents take actions
- Enterprise MCP and A2A Tools Access — connect agents to internal tools with least-privilege permissions
Per-user access control with encrypted permission keys, audit trails on every action, 86+ MCP tools (task management, CRM, alarm scheduling, push notifications, system monitoring), per-user SQLite databases. Agents inherit the user’s permissions. One identity, one set of rules, full accountability.
Where They Meet
ClearML’s lifecycle: Data → Experiment → Train → Deploy → Serve → Monitor.
WorkingAgents’ lifecycle: Permission → Route → Execute → Log → Schedule → Notify.
ClearML ends where WorkingAgents begins. ClearML gets the model deployed and serving. WorkingAgents governs the agents that consume those models and act on their outputs. The gap between “model is serving” and “agent is operating safely in production” is exactly what WorkingAgents fills.
Synergy Areas
1. ClearML Model-as-a-Service + WorkingAgents LLM Routing
ClearML deploys LLMs with one click on any infrastructure. WorkingAgents routes agent requests to LLMs with per-user permissions. The integration:
- ClearML deploys multiple models (DeepSeek for cost-sensitive tasks, GPT for complex reasoning, domain fine-tuned models for specific clients)
- WorkingAgents’ LLM routing layer selects the right model per request based on agent permissions, task type, and cost policy
- Per-user access control determines which models each agent can access — a support agent gets the small model, a research agent gets the large one
- ClearML handles model lifecycle (deployment, scaling, versioning). WorkingAgents handles agent lifecycle (permissions, routing, auditing).
ClearML’s Model-as-a-Service becomes the model backend. WorkingAgents becomes the governed model frontend. Enterprises get one-click deployment AND governed access in a single stack.
2. Infrastructure Control Plane + Agent Operations Layer
ClearML’s Infrastructure Control Plane optimizes GPU utilization with fractional GPUs, quota management, and multi-tenant scheduling. WorkingAgents adds the agent operations layer on top:
- Quota-aware agent scheduling — WorkingAgents knows each tenant’s GPU quota from ClearML. When an agent requests a training job, WorkingAgents checks the tenant’s remaining quota before submitting to ClearML. Over-quota requests are blocked at the agent layer, logged, and the responsible team is notified via Pushover.
- Fractional GPU governance — ClearML can allocate fractional GPUs. WorkingAgents governs which agents can request full GPUs vs. fractions. A research team’s agent gets full GPU access for training. A development team’s agent gets fractional access for experimentation. Permission-enforced, not just policy-documented.
- Cost tracking and alerting — ClearML provides utilization data. WorkingAgents ingests it into per-user databases, creates cost tracking tasks, and schedules alarms when spending exceeds thresholds. The agent that triggered the compute gets audited. The team lead gets a push notification.
ClearML optimizes the infrastructure. WorkingAgents governs who uses it, how much, and for what.
3. Experiment Management + Agent-Driven MLOps
ClearML’s experiment tracker logs every training run — hyperparameters, metrics, artifacts. But who triggers experiments? Who reviews results? Who decides to retrain?
WorkingAgents automates the human workflow around experiments:
- Model performance drops below threshold → ClearML detects the drift → WorkingAgents receives the alert → creates a task “Investigate model drift for Customer X” → assigns to the ML engineer → schedules a follow-up alarm in 24 hours → if not resolved, escalates via push notification
- New training data arrives → WorkingAgents’ alarm triggers a scheduled retraining job → submits to ClearML’s orchestration → ClearML executes the training → WorkingAgents tracks the job, logs the result, notifies the team, and updates the NIS CRM with the client interaction
- A/B test results are ready → ClearML has the metrics → WorkingAgents creates a decision task with deadline → if no decision by the deadline, promotes the winner automatically based on pre-configured rules
ClearML tracks what happened. WorkingAgents ensures someone acts on it.
4. GenAI App Engine + Agent Governance
ClearML’s GenAI App Engine lets business stakeholders deploy and iterate on GenAI applications. But GenAI apps in production need governance:
- Which users can access which GenAI app? WorkingAgents’ per-user permissions.
- What data can the GenAI app access? WorkingAgents’ tool-level access control.
- Who gets notified when the app fails? WorkingAgents’ alarm system.
- What happens when the app produces results? WorkingAgents’ task manager schedules follow-up actions.
- How do you audit what the app did? WorkingAgents’ per-action audit trail.
ClearML deploys the GenAI app. WorkingAgents wraps it in governance. Business stakeholders get self-service GenAI with guardrails — they can iterate on applications without IT worrying about unauthorized data access or ungoverned actions.
5. Multi-Tenancy Alignment
Both platforms implement multi-tenancy independently:
| Capability | ClearML | WorkingAgents |
|---|---|---|
| Isolation | Isolated networks and storage per tenant | Per-user SQLite databases |
| Access control | RBAC, SSO, LDAP | Per-user encrypted permission keys |
| Quota management | GPU quotas per tenant | Tool-level permissions per user |
| Audit | Experiment logs, compute usage | Action logs, task provenance |
The integration maps ClearML tenants to WorkingAgents users. Same identity, same boundaries, same governance — from GPU allocation to agent behavior. A tenant in ClearML is a user in WorkingAgents. Their GPU quota in ClearML maps to their tool permissions in WorkingAgents. One identity system governs both layers.
6. Open-Source Alignment
ClearML’s open-source foundation (16K+ GitHub stars) aligns with WorkingAgents’ approach. Both products benefit from transparency:
- ClearML’s open-source experiment tracker integrates with WorkingAgents’ MCP tools — any ClearML API can be wrapped as an MCP tool
- WorkingAgents’ Elixir codebase is inspectable — enterprises verify that permission enforcement works as documented
- Joint open-source integration: a ClearML MCP tool package that gives any MCP-connected agent governed access to ClearML’s experiment tracking, model deployment, and infrastructure management
The open-source bridge lowers the integration barrier. An AI builder already using ClearML adds WorkingAgents governance by connecting to an MCP server — no SDK changes, no API wrappers, no custom integration code.
7. The 300,000 AI Builder Opportunity
ClearML has 300,000 AI builders using the platform. These builders are deploying models that increasingly operate as autonomous agents. Every one of them faces the same question: “How do I govern what my agent does in production?”
ClearML’s answer is infrastructure governance — GPU quotas, RBAC, experiment tracking. WorkingAgents’ answer is agent governance — tool permissions, action auditing, scheduled follow-ups, escalation chains. The 300,000 builders need both. ClearML is the distribution channel. WorkingAgents is the value-add.
The Partnership Opportunity
For ClearML: WorkingAgents extends the platform from “AI infrastructure” to “AI operations.” ClearML gets models deployed and GPUs optimized. WorkingAgents governs the agents consuming those models and acting on their outputs. The gap between model serving and agent operations is the next frontier for their 2,100 customers.
For WorkingAgents: ClearML solves the model lifecycle problem. WorkingAgents needs models deployed, versioned, and served. ClearML does this with one click on any infrastructure. Instead of integrating each model provider separately, one ClearML integration gives WorkingAgents access to any model on any infrastructure — with GPU optimization built in.
For the joint customer: A unified AI stack — ClearML manages infrastructure and model lifecycle, WorkingAgents manages agent behavior and operational workflows. Models are deployed efficiently (ClearML), routed intelligently (WorkingAgents), accessed with permissions (WorkingAgents), computed on optimized GPUs (ClearML), and every action is audited end-to-end.
Concrete Next Steps
- MCP tool integration — Wrap ClearML’s REST API as WorkingAgents MCP tools: experiment tracking, model deployment, GPU status, queue management, task submission. Estimate: 3-4 days for 8-10 tools.
- Model-as-a-Service routing — Connect WorkingAgents’ LLM routing to ClearML-deployed models. WorkingAgents selects the model, ClearML serves it. Estimate: 2 days for the provider adapter.
- Joint MLOps workflow demo — Model drift detected by ClearML → WorkingAgents creates investigation task → schedules retraining → ClearML executes → WorkingAgents validates and notifies. End-to-end automated MLOps with governance.
- Open-source ClearML MCP package — Publish a community MCP tool package for ClearML, giving 300,000 builders governed access to ClearML through any MCP-connected agent.
ClearML gets AI from experiment to production in one platform. WorkingAgents gets AI agents from deployment to governed operations in one control plane. ClearML optimizes what AI runs on. WorkingAgents governs what AI does. For the enterprise deploying autonomous AI agents at scale, the infrastructure platform and the governance platform are two halves of the same requirement.