Tag Archives: AIGovernance

From MLOps to LLMOps to AgentOps: Building the Bridge to Autonomy

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We didn’t just upgrade models—we changed the discipline. What used to be “model lifecycle management” is now autonomy lifecycle management. And with that, enterprises are facing a truth most haven’t yet operationalized: we now live in three overlapping worlds—Traditional AI, GenAI, and Agentic AI—each with its own workflow logic, tooling, and governance.

In traditional MLOps, workflows were deterministic: data in, prediction out. Pipelines were clean, measurable, and managed through platforms like MLflow, Kubeflow, BentoML, or Evidently AI. We focused on reproducibility, accuracy, and drift detection—predictable systems built for static decisions.

Then came LLMOps, and the equation broke. We moved to unstructured data, prompts, RAG, and safety filters. Non-deterministic outputs meant no two runs were ever the same. Suddenly, we were tracking token costs, hallucination rates, latency SLOs, and human feedback loops in real time—using stacks like LangChain, LlamaIndex, PromptLayer, Weights & Biases, and Credo AI.

Now we’re entering AgentOps—the autonomy layer. Systems act, reason, and collaborate through orchestrators like LangGraph, CrewAI, or AutoGen. AWS is already positioning AgentCore (on Bedrock) as the enterprise runtime—agents with persistent memory, context, and real-time observability. But the architecture shift isn’t just technical; it’s organizational. The winning model is “federated”: specialized teams with unified observability across all three layers—AI, GenAI, and Agentic AI.

When I sit with exec teams, I see the same pattern: most can build great models, but few can run parallel operational capabilities at once. And that’s the new muscle—keeping deterministic, generative, and agentic systems aligned under one governance fabric.

What makes the difference isn’t the flashiest demo; it’s boring excellence—clear SLOs, version control, cost discipline, and behavioral guardrails. That’s how we turn agents into trusted co-workers, not expensive chaos engines.

So here’s the question I leave leaders with: If your org had to strengthen just one layer this quarter—MLOps predictability, LLMOps safety, or AgentOps autonomy—where would you start, and how ready is your team to run all three in parallel?

Data Mesh was step one. 2026 belongs to agent ecosystems.

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I used to think “more catalogs, better lakes” would get us there. Then I watched agents start acting—not just assisting—and realized our data products weren’t ready for that responsibility.

Here’s the simple truth I’m seeing with executive teams: bad data becomes bad decisions at scale. If our contracts, SLOs, lineage, and internal marketplaces are weak, agents will scale the wrong thing—errors—at machine speed. That’s a board-level conversation, not an IT complaint.

What changes in practice?
We evolve the data operating model from “publish & pray” to agent-grade: data products with p95 latency targets, explicit access scopes, and traceable provenance. Hyperscalers are now shipping real agent runtimes (memory, identity, observability—and billing), which means the economics and accountability just got very real.

How I’m approaching it with leaders:

  • Certify data products for agents. Each product has an owner, SLOs (latency/freshness), and mandatory provenance. If it can’t meet its SLOs, it doesn’t feed agents—full stop.
  • Enforce least privilege by skill. Approvals are tied to the actions an agent can perform, not just the datasets it can see.
  • Make observability a product. Trace every call (inputs, tools, sources, cost, outcome). No trace, no production.

Practical next steps:
Start by mapping your top 10 data products to target agent skills and auditing them. Set SLOs. Assign owners. Then pick one product—implement policy-aware access and lineage capture, record evaluation traces for every agent call, and scale it. Afterwards, launch an internal Agent Marketplace that connects certified skills and certified data products, with change gates based on risk tier.

KPIs I push for:

  • % of agent invocations served by certified data products meeting SLOs (with recorded lineage)
  • $/successful agent task at target quality and latency
  • Incident rate per 1,000 runs (blocked vs executed)

Behind the scenes, the shift that surprised me most wasn’t technical—it was managerial. The winning teams treat this as work redesign: new ownership, new runbooks, new kill criteria. When we do that, agents unlock speed and resilience. When we don’t, they magnify our mess.

If you had to fix just one weak link this quarter—SLOs, provenance, or access controls—which would it be, and why?

Ready for EU AI Act? Your framework probably isn’t. Here’s why.

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I’ll be honest—I’ve watched too many smart teams stumble here. They bolt GenAI onto legacy model risk frameworks and wonder why auditors keep finding gaps. Here’s what I’m seeing work with CDOs navigating the EU AI Act:

You need segmentation, not standardization. Traditional ML, GenAI, and agents carry fundamentally different risks. Treating them the same is like using the same playbook for three different sports.

Start with an AI Management System — ISO/IEC 42001 for structure, 42005 for impact assessments, 42006 for auditability. Map it to NIST’s GenAI Profile + COSAIS overlays. This isn’t box-checking; it’s how you govern at scale without chaos.

Then segment your controls: ML needs drift monitoring and data quality checks. GenAI needs prompt-injection defenses and hallucination tracking. Agents? Autonomy caps, tool allow-lists, human-in-the-loop gates, sandboxed execution, full action logs. Use OWASP’s LLM Top 10 — your security team already speaks that language.

On EU AI Act compliance: GPAI obligations are phasing in now. Inventory your systems, classify them (general-purpose, high-risk, other), run fundamental rights impact assessments for high-risk deployers, then choose your conformity path. Don’t wait.

Make it operational. Name control owners. Set SLAs. Track what matters—prompt-injection incidents, drift rates, task success, hallucination coverage, adoption rates, cycle-time savings. Require evidence (model cards, eval runs, logs) before promotion. Gate agent autonomy upgrades.

And frankly, treat anonymization as something you prove, combining technical (DP, SDC, k-anon) with organizational and process controls. Keep DPIA’s records updated per EDPB/ICO guidance.

If you’re piloting agents: cap autonomy first, scale second.

The teams moving fastest with focus aren’t skipping controls—they built the right ones from day one.

Which KPI tells you the most about your AI program’s health—risk metrics, performance indicators, or value creation? I’m especially curious what agent pilots are tracking beyond the basics.

EU AI Act´s General-Purpose AI Models (GPAI) Rules Are Live: How to prove Compliance next months.

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EU obligations for general-purpose AI kicked in on 2 Aug 2025. Models already on the market before 2 Aug 2024, must be fully compliant by 2 Aug 2027 – but boards won’t wait that long.

Over the past few weeks I’ve sat with product, legal, and model teams that felt “compliance-ready” … until we opened the evidence drawer. That’s where most programs stall. The good news: the playbook is clear now. GPAI Code of Practice (10 Jul 2025) gives a pragmatic path, and the Guidelines for GPAI Providers (31 Jul 2025) remove a lot of scope ambiguity. Voluntary? Yes. But it’s the fastest way to show your house is in order while standards mature.

Here’s how I’d tackle this —no drama, just discipline. First, align on who you are in the Act (provider vs. deployer). Then make one leader accountable per model and wire compliance into your release process.

My advice, Companies should:

  • Gap-assess every in-scope model against the Code. Do you have a copyright policy, a training-data summary, documented evals, and a working view of downstream disclosures? If any of those are fuzzy, you’re not ready.
  • Stand up model cards and incident logs; add release gates that block launch without evidence. Map risks to your cyber program using CSF 2.0 so Security and Audit can speak the same language.
  • Run an internal GPAI evidence audit. Publish an exec dashboard with: % of models with complete technical files and disclosures, incident MTTD/MTTR, and time-to-close regulator/customer info requests.

A quick reality check: big providers are splitting—some signalling they’ll sign the Code, others not. That’s strategy. Your advantage (especially if you’re an SME) is disciplined documentation that turns “we promise” into procurement-ready proof.

My rule of the thumb: if the CEO can’t see weekly movements on documentation completeness and incident handling, you are in pilot land – no matter how advanced the model sounds.

What would you put on a one-page dashboard to convince your CFO – and your largest EU customer – that your GPAI program in truly under control?