Tag Archives: AgenticAI

I Just Analyzed the Hyperscalers’ Agent Platforms. Here’s What Shocked Me

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I’ve been deep-diving into what AWS, Azure, and Google are actually building for AI agents—and I’ll be honest, there’s a fundamental shift happening in how we’ll build autonomous systems. The three hyperscalers are making radically different bets on the future of work.

The Reality Check

All three major hyperscalers—AWS, Microsoft Azure, and Google Cloud Platform—provide full-stack solutions encompassing orchestration, deployment, security, observability, and integration capabilities. The choice between platforms increasingly depends on existing enterprise infrastructure, specific framework preferences, required runtime characteristics, and ecosystem integration needs rather than fundamental capability gaps.

When I first examined AWS AgentCore, I thought “this is impressive infrastructure.” The most mature marketplace ecosystem. The deepest tooling, built on 15+ years of cloud services. Eight-hour runtimes, isolated microVMs, seven integrated services. Then I realized—they’re not just building faster AI. They’re building systems that can actually think and act over extended periods.

Azure took a different path. They’re saying: “Most enterprises live in the Microsoft 365 ecosystem. Let’s embed agents there with built-in identity management and multi-agent orchestration.” Smart. Pragmatic. Different.

Google’s playing an interesting game. They’ve released the Agent Development Kit with persistent memory and an open protocol called A2A. Translation? They’re betting that agent interoperability and a framework-agnostic approach are the real competitive advantages.

Why This Matters for You

The marketplace is projected to hit $163 billion by 2030, with agents representing $24.4 billion. But numbers don’t tell the real story.

What matters is this: companies building internal agent marketplaces—treating agents as managed products with governance frameworks—are quietly pulling ahead. They’re not running scattered pilots anymore. They’re deploying reusable agents as organizational assets.

Most enterprises? Still stuck asking “should we build an agent?” Meanwhile, forward-thinking organizations are asking “how do we govern and orchestrate dozens of them?”

The Three Philosophies

AWS is betting on depth and runtime longevity. Azure is betting on ecosystem integration. Google is betting on openness and protocol standardization.

None of them are wrong. Your choice depends on where your organization lives today—and where you want it to be in 2027.

Here’s My Honest Take

The adoption of agentic AI—evidenced by marketplace growth projections and partner ecosystem expansion—signals that enterprises are moving from experimentation to production deployment.

Fifteen months ago, the conversation was “generative AI or not.” Today, it’s “which hyperscaler’s agent architecture aligns with our strategy?” That’s progress. But it also means the clock is ticking.

The gap between experimentation and real deployment is widening. Organizations that figure out governance, lifecycle management, and internal marketplaces now won’t just be faster—they’ll build moats their competitors can’t cross.

Where’s your organization in this journey? Are you still in pilot mode, or have you started thinking about what production-scale agent governance actually looks like?

I’m genuinely curious. Drop a comment.

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?

Agentic Operating Models: from Pilots to P&L

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We’re past the demo phase. Boards are asking a harder question: how do human-plus-agent workflows show up in cash flow—this quarter? There is a clear answer: The winners don’t “add an agent”; they redesign the work. That means owners, SLAs, guardrails, and value tracking—weekly. Not glamorous, just effective.

Here’s the short playbook I’d bring to the next ExCo:

  • Make Agents products. Name a product owner, publish SLAs (latency, accuracy, human-override rate), and set chargeback so value—and cost—land in the P&L.
  • Design human+agent flow, end-to-end. Pilots fail for organizational reasons. Tie every pilot to a customer metric and a service level from day one.
  • Build guardrails you can audit. Map risks to NIST’s Cyber AI Profile; log decisions, provenance, and incidents. “Trust” that isn’t evidenced will stall at Legal.

Does it pay?  Signals are real but uneven. A European bank modernization program cut 35-70% cycle time with reusable “agent components.” In KYC/AML, agent “factories” show 200-2000% productivity potential when humans supervise at scale. Klarna’s AI assistant handles  ~1.3M monthly interactions (~800 FTEs) with CSAT parity. Yet BCG says only ~5% are truly at value-at-scale, and Gartner warns ~40% of agentic projects could be scrapped by 2027. Operating model discipline determines who wins.

If I had 90 days:

  • 30: Inventory top 5 agent candidates; assign owners; baseline SLAs and override rates.
  • 60: Stand up an Agent Review Board (CIO/CDO/GC/CISO); add release gates and rollback.
  • 90: Ship two agents to production; publish a value dashboard (savings, cycle time, SLA hit rate) and decide scale/retire.

A candid note on risk: labor anxiety and model drift will erase ROI if we skip change management and runtime oversight. Bring HR and the 2nd line in early, and rehearse incidents like you would a cyber tabletop.

If we can’t show weekly value, SLA adherence, and audit-ready evidence, we’re still in pilot land—no matter how advanced the model sounds.

What would make your CFO believe – tomorrow – that an agent belongs on the P&L?

Agentic Mesh or Just Another Buzzword? Cutting Through the Hype

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Let’s be honest: most of us have sat through AI demos that looked impressive… and then quietly died in the pilot graveyard. Why? Because smarter models alone don’t create enterprise value. The real shift is moving from shiny pilots to system-level architectures—what McKinsey calls the Agentic Mesh.

I’ve seen this firsthand. When teams focus only on “better models,” they often miss the harder (and less glamorous) work: wiring agents together, defining guardrails, and making sure actions are auditable. That’s where scale either happens—or fails.

What are we learning as an industry?

  • Models matter, but architecture and process discipline matter more.
  • Standards like MCP and A2A are becoming the “USB-C of AI,” cutting down brittle integrations.
  • Governance isn’t optional anymore—ISO/IEC 42001, NIST AI RMF, and “human-on-the-loop” ops are quickly becoming the baseline.
  • We have to treat agents like digital colleagues: assign roles, permissions, even offboarding procedures.
  • And without proper observability—AgentOps, logs, kill-switches—autonomy can turn into automated chaos.

For executives, here’s what I’d do today if I were scaling this in your shoes:

  1. Name it. Create a platform team that owns the “mesh”—protocols, policy engines, memory hubs, observability.
  2. Start small, but measure big. Choose a few revenue- or cost-linked workflows, run shadow/canary pilots, and track hard KPIs.
  3. Bake in governance early. Build an agent registry, enforce least-privilege access, and red-team agents before production.
  4. Scale with discipline. Treat agent patterns like products—documented, reusable, and measured.

Here’s my takeaway: the winners won’t be those with the smartest model, but those who can turn agents into an integrated, trusted system—a digital workforce that’s secure, observable, and genuinely valuable.

👉 What’s been your biggest blocker moving from pilots to scaled AI systems—technology, governance, or people?

Beyond Chatbots: Why Agentic AI Will Redefine Your Operating Model

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We’re moving beyond the chatbot phase into something much more transformative: autonomous AI agents that can actually get work done. Agentic AI isn’t just another tool to bolt onto existing processes. It’s fundamentally changing how businesses operate. AI agents can plan their own workflows, make decisions across multiple systems, and interact with everything from APIs to documents to other agents.

But here’s what I’ve learned from different implementations in clients: the winners aren’t just deploying agents. They’re rethinking their entire operating model.

What Actually Works

The companies getting real results are doing a few things differently. First, they’re designing workflows with agents in mind from the ground up, not trying to retrofit existing processes. Although, some companies are still working through cultural and change management barriers, focusing on measurement, and strong leadership to realize real value from AI technologies. This makes sense when you see it in action.

Second, they’re taking governance seriously. You need clear boundaries on what agents can do, audit trails, and fallback procedures. The “Wild West” approach doesn’t work at enterprise scale.

Third, they’re building for interoperability. The real value comes when agents can work together through standardized protocols (e.g., A2A, MCP). The emerging orchestration layers are making this possible. They are both critical enablers for scaling agent ecosystems safely.

The ROI Reality Check

The consulting firms love to throw around impressive numbers, and I’ve seen some compelling case studies. They point to measurable improvements intime-to-market and efficiency. But the real question is whether these gains hold up when you scale beyond pilot projects.

From what I’m seeing, the answer is yes—but only if you’re willing to rethink roles and responsibilities. We’re talking about new job categories: people who can design agent workflows, architects who can orchestrate human-AI collaboration, product owners who understand both business needs and AI capabilities.

The Strategic Question

If you’re a CDO or digital transformation leader, you’re probably already getting questions about this from your board. The technology is moving fast, but the organizational change is the real challenge.

The question isn’t whether agentic AI will transform how we work—it’s whether your organization will be ready when it does. Are you building the capabilities to orchestrate humans and AI agents effectively? Because that’s where the competitive advantage will come from.

What’s your experience been with autonomous AI agents? I’m curious to hear how other organizations are approaching this transition.