[ AGENTOPS ]
AgentOps: Production Reliability for AI Agents
Most agentic projects stall in pilot. Not because the agents do not work, but because nobody owns what happens after the demo. There is no on-call. No drift detection. No audit trail when an agent makes the wrong call at 2am. AgentOps is what you build to fix that.
[ THE LEVENT POINT OF VIEW ]
MLOps grew up. AgentOps is its descendant.
The discipline did not start with agents. It started with classical ML: model registries, feature stores, drift detection, retraining pipelines. We have spent fifteen years inside enterprise ML platforms keeping models alive in production. Models are now decisions. Decisions need observability, audit trails, and rollback. The agentic-native consultancies appearing in 2024 and 2025 do not have this lineage. We learned on the systems underneath them.
[ WHAT THIS MEANS IN PRACTICE ]
Agent run observability is the foundation. Every tool call, every memory read, every model invocation gets traced. When an agent makes the wrong decision in production, you need to answer "what did it see, what did it call, what did it return" within minutes, not days. Token-cost optimisation comes next: a careless agent on a frontier model can spiral fast. We instrument budgets per agent, per tenant, per use case.
Drift and evaluation runs continuously. Agent outputs degrade as the world changes. We run scheduled evaluation against held-out test cases and flag regression before users do. Tool registry maintenance, incident response runbooks, compliance and audit logging round out the discipline. Each one is a place where pilots quietly die between week six and week eighteen.
Rollback is the question most teams cannot answer. "Rolling back an agent" is not the same as redeploying yesterday's container. The Skills, the prompt registry, the tool versions, the memory bank, the retrieval index, and the underlying model are six independent surfaces, each with its own deployment history. We version every surface, log every change against a single deploy ID, and design the rollback path as a first-class operation, not an afterthought triggered at 2am.
FinOps for agents is a new discipline that most teams discover the hard way. A single agent loop that fans out to fifteen tool calls on a frontier model can cost more per hour than a small EC2 fleet. We instrument cost per run, per intent, per tenant. We surface anomalies before the bill does. And we tune the architecture (smaller models on the cheap paths, frontier models on the consequential paths) so the cost profile matches the business profile.
The convergence framework that has emerged across the industry (Contextualize, Harmonize, Anticipate, Negotiate, Generate, Evolve) maps cleanly onto our five-pillar service model. ServiceNow published their version in early 2026; Everest Group published a similar one. Our delivery aligns with both. The labels matter less than the discipline.
[ HOW WE DELIVER THIS ]
How we deliver this
Operate is the lifecycle pillar that owns AgentOps engagements: observability stack, instrumented tool calls, evaluation harness, runbooks. For organisations that want the outcome without standing up an internal team, Managed Service runs AgentOps as a turnkey service. The marquee proof is a national energy company in the UAE, where we operate the AI platform across 15+ business entities under a Managed Service contract.