When agents can use tools, update records, draft messages and trigger workflows, leaders need visibility and control. Mach Lilies implements the registry, permissions, evaluations, logs, approvals, monitoring, incident response and evidence packs that make agents safe to operate. Mach speed. Lily craft.
AgentOps turns AI agents from risky experiments into manageable operational assets.
Most agentic projects stall not because the model is weak, but because no one can see what the agents can access, what they did, where they failed, who approved them, or what they cost. AgentOps is the operating layer that answers those questions. Mach Lilies builds it as part of delivery — a control tower for production AI that gives operations, technology and risk leaders the visibility and the brakes they need.
The operating layer for production AI agents — implemented in your environment, not described on a slide.
A single inventory of every agent: what it does, which workflow it serves, what it can access, and who owns it.
Least-privilege tool and data boundaries for each agent, so it can only ever act inside its workflow.
Human-in-the-loop checkpoints for high-risk actions — review queues, sign-off and clear escalation paths.
A complete, queryable record of every tool call, decision, escalation and approval — built for audit.
Test suites that check prompts, models and workflows against known cases before and after every change.
Dashboards for quality, throughput and spend, with model routing and cost controls so usage stays predictable.
Pause, roll back or restrict an agent when behaviour changes — with a defined incident process.
Quarterly reporting that gives leaders and auditors a clear, current view of every agent in operation.
Four movements from agents you can't see to agents you can govern with confidence.
Register what's running, what it can touch and who owns it — the baseline for any control.
Apply least-privilege permissions, approval gates and escalation rules to each workflow.
Wire in action logs, evaluation suites and dashboards so quality and cost are visible.
Run monitoring, regression checks and incident response, with reporting for leaders and auditors.
For the people accountable when AI acts inside the business — technology, operations, risk and compliance.
We build governance into the build, not as an afterthought once something has gone wrong.
The control layer works across GPT, Claude, Gemini, open-source and private deployments.
Logs and reports are structured for audit and assurance from day one.
Your team can run the control tower — we hand over dashboards, runbooks and process.
Know what your agents can access, what they did, where they failed, who approved them, and what they cost.
AgentOps is the operational layer for production AI agents — the registry, permissions, monitoring, evaluation, audit logs, approval workflows, cost controls and incident response that let an organisation run agents safely and account for what they do. It is to AI agents what DevOps and MLOps are to software and machine learning.
MLOps keeps models trained, deployed and monitored. AgentOps governs what agents are allowed to do once they can take actions — using tools, updating records and triggering workflows. It adds permissions, human approval, action audit trails and incident controls on top of monitoring.
Even one agent that can act inside business systems needs boundaries, logging and a way to pause it. AgentOps scales down to a single workflow and grows with you as more agents go into production.
Yes. The control layer is model- and vendor-agnostic. We can wrap governance around existing agents, regardless of how or where they were built.
Every tool call, decision, escalation and human approval — structured so that, for any action, you can answer what happened, why, who approved it and what it cost. That record is what makes agents defensible to auditors and regulators.
Typically an implementation engagement to stand up the control layer, followed by a managed governance retainer with quarterly reporting. We scope to the number of agents and the level of oversight your risk profile requires.
Tell us which agents or AI workflows are live or planned, the systems they touch, and your risk and compliance concerns. We reply to every serious enquiry within one business day.
