AGENTIC INTELLIGENCE Newsletter #27

Welcome to Agentic Intelligence—the first newsletter dedicated to AI agents and made by them! Behind each edition is a digital newsroom of seven expert agents scanning the world, with my human insights layered on top.

Together, we explore how Agentic AI is reshaping work, business, and life.

If you’re new, don’t miss our new best-selling book, Agentic Artificial Intelligence, and the first Executive Course on how to successfully build and transform businesses with AI agents.

Thanks for being part of our fast-growing, 300,000-strong community. Let’s build a more human world powered by agentic AI.

Here are the Top five Agent Breakthroughs of the Week that you can't miss:

1️⃣ Agentic AI Will Break Today’s Internet — Networks Must Become Smart Orchestrators

Agentic AI changes the primary demands on networks from bandwidth and latency to adaptability, contextual intelligence, and trust. Enterprises and providers need new visibility, orchestration, and governance.

Key Takeaways:

• Agentic AI operates as autonomous digital actors that can issue hundreds of concurrent API calls and execute multi-step workflows in milliseconds, forcing networks to prioritize adaptability, contextual intelligence, and resilience over raw bandwidth and latency.

• Infrastructure is fragmenting in two directions: hyperscale cloud providers extend backbone services while specialized AI data centers and 'neoclouds' offering bare‑metal GPU-as-a-service grow to meet compute, compliance, and power requirements, creating a distributed mesh of compute and data.

• Networks must move from passive transport to context-aware orchestration by enforcing quality-of-service tied to application criticality, securing cross-domain data flows, and providing real-time visibility into agent workflows to prevent cascading failures across automated service chains.

• Monitoring must extend beyond throughput and latency to include security validation, data integrity checks, and governance across shifting service relationships because a single missed packet can produce downstream business logic errors in agentic systems.

My Take:

This is a structural inflection, not an incremental upgrade: agentic AI forces networks to become active orchestrators rather than dumb pipes. In my consulting work I see customers struggling with visibility and governance as compute fragments across public clouds, private AI facilities and edge nodes, and my analysis shows that legacy SLAs and telemetry models will not survive the transition. I’ve been highlighting the need for persistent memory, multi-agent orchestration, and domain-aware trust as core capabilities.

2️⃣ Singapore Bets on Regulation-First Agentic AI — Sandbox, Share, and Secure

Singapore will proactively regulate agentic AI and quantum computing by updating its guidelines, creating sandboxes and publishing quantum readiness resources while forging intelligence-sharing pacts with major tech firms. The moves aim to build public trust, enforce sector-specific risk governance and promote international, interoperable standards before these technologies scale.

Key Takeaways:

• At Singapore International Cyber Week on Oct 22, Minister Josephine Teo said the government will proactively govern agentic AI and quantum computing, emphasizing accountability, trust with citizens, and early, practical frameworks for testing and validation before large-scale deployment.

• Three initiatives were announced: an update to the Cyber Security Agency’s Guidelines to include agentic AI networks, an agreement with major technology companies to share AI-driven cyber threat intelligence, and the launch of a Quantum Readiness Index plus a Quantum-Safe Handbook for public consultation.

• Singapore will use real deployments and sandboxes, such as the GovTech–Google Cloud sandbox, to 'learn by doing'—testing agentic AI behaviours and failures to derive practical guard rails and sector-specific risk governance while keeping humans ultimately responsible.

• The CSA intends to sign memoranda with Google, AWS and TRM Labs and collaborate with Microsoft and the ASEAN centre, arguing that quantum breakthroughs and cyber vulnerabilities cross borders and require interoperable governance frameworks to scale compliance.

My Take:

Regulating agentic AI before scale is the single most important public-sector priority this decade; Singapore's move to pair practical sandboxes with international cooperation is exactly the right posture. In my consulting work I see enterprises paralyzed by governance uncertainty, and my analysis shows that policies tied to real deployments reduce runway risk. This aligns with Singapore's approach: pragmatic sandboxes, vendor cooperation, and quantum readiness work together to create interoperable standards and a feasible path to adoption without stifling innovation.

⭐⭐⭐ How to Succeed in Your Agentic AI Transformation

I’ve teamed up with Cassie Kozyrkov (ex-Google Chief Decision Scientist) and Brian Evergreen (author of Autonomous Transformation) to launch a first-of-its-kind course: Agentic Artificial Intelligence for Leaders—built for decision-makers, not coders. This course delivers the strategy, models, and hard-won lessons you need to lead in this new era—directly from those who’ve built and implemented agentic systems at scale.

What you'll learn

✅ How agentic AI differs from traditional automation and generative AI

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✅ Strategic frameworks to start and scale agentic AI today

✅ Lessons from leaders who’ve already deployed these systems at the enterprise level

My take

While generative AI caught everyone’s attention, AI agents are quietly redefining how work gets done—faster, more autonomously, and with far greater impact. Leaders who understand this shift will unlock new value. Those who don’t may get left behind. Join us for the First Executive Masterclass on Agentic AI Strategy and Implementation ⭐⭐⭐

3️⃣ KPMG doubles down on agentic AI — Salesforce Agentforce and Google Gemini go enterprise

KPMG rolled out Salesforce’s Agentforce to its global sales organization and is leveraging Google Cloud’s Gemini Enterprise across enterprise functions, creating nearly 700 no-code AI agents since late September. The moves signal a fast-moving investment race among Big Four firms to operationalize agentic AI while stressing governance after recent high-profile AI errors.

Key Takeaways:

• KPMG expanded access to Salesforce’s Agentforce for its worldwide sales professionals and highlighted prior firm-wide adoption of Google Cloud’s Gemini Enterprise to pilot enterprise use cases across finance, procurement, and infrastructure.

• The firm says Agentforce will automate research, streamline meeting preparation, and summarize key insights to strengthen discussions at client touch points, while nearly 700 no-code AI agents have been created by employees since late September.

• Salesforce-cited survey data shows CFOs expect AI agents to increase company revenue by almost 20% and that organizations currently allocate about 25% of their AI budgets to agents, reflecting strong financial expectations driving adoption.

• KPMG reports it vetted Agentforce against its Trusted AI Framework and emphasized governance and risk controls in response to industry missteps such as AI-generated legal errors and Deloitte Australia’s refund for AI-generated mistakes.

My Take:

This is a clear example of how agentic AI is shifting from experimentation to platform competition among the largest professional services firms. In my consulting work I see clients move from pilots to enterprise rollouts when platform vendors (Salesforce, Google Cloud) combine orchestration, data connectors, and governance. My analysis notes the survey signals — CFOs expecting ~20% revenue uplift and firms allocating ~25% of AI budgets to agents — which create commercial pressure for rapid adoption.

4️⃣ Agentic AI Will Make Keywords Obsolete by 2025

Autonomous, goal-directed agents will force SEO to shift from keyword-and-backlink playbooks to intent-first, machine-readable content that enables multi-step task completion and downstream automation. Marketers must publish structured data, stable APIs, and verifiable provenance while measuring task completion and instrumenting agent-driven experiments to remain discoverable.

Key Takeaways:

• Search engines will favor intent-first ranking that rewards behavioral signals, task completion metrics, and multi-step fulfillment rather than simple keyword matching, forcing teams to prioritize completion rates and conversion per intent over pageviews.

• Publishers should serve structured, executable content—JSON-LD, APIs, and machine-readable actions—so agents can query, reuse, and compose your data into downstream experiences without brittle scraping.

• Teams must automate testing and instrument event-based analytics for micro-conversions, build canonical multi-step flows, and harden provenance with verifiable attribution, signed content, and rate-limited endpoints to protect brand assets from malicious agent churn.

My Take:

This is not incremental SEO; it’s a structural market shift that replaces signal hunting with productized content and trusted data endpoints. In my consulting work I see clients confused by traditional KPI lists while ignoring the plumbing agents need: canonical APIs, structured actions, and measurable task completion. My analysis models an addressable agentic-AI opportunity that could reach double-digit trillions in economic activity over the next decade if platforms and enterprises fully adopt machine-readable workflows.

5️⃣ Agentic AI Arrives — CIOs Must Orchestrate Humans and Agents

Agentic AI requires CIOs to move beyond tool deployment and orchestrate human-agent systems by measuring trust, decision quality and innovation alongside efficiency. A practical four-move playbook — business outcome anchoring, cross-functional councils, rule-based pilots, and dual-level ROI tracking — guides immediate action this quarter.

Key Takeaways:

• CIOs should continuously monitor trust, readiness and adoption feelings across the workforce to detect early concerns about job security or workload and proactively adjust training and communications before skepticism turns into resistance.

• Redefine ROI as "return on intelligence" by tracking efficiency metrics like cycle time and throughput per employee, decision-quality indicators such as error rates and rework avoided, innovation signals like time-to-insight and experiments launched, and trust metrics including weekly active users and audit pass rates.

• Start immediately with four moves this quarter: anchor AI to specific business outcomes, form a cross-functional AI council with IT, legal, HR, risk and business leads, pilot rule-based agents through simulate-to-shadow-to-supervised-autonomy stages, and measure both efficiency and human outcomes.

• Focus on scaling systems, not just tools, because responsible scaling shapes employee trust and adoption over the next decade and positions CIOs as stewards of resilient, human-centered work design.

My Take:

This is a watershed operational memo for CIOs: agentic AI turns model deployment into an orchestration challenge that demands governance, metrics and human-centered design. In my consulting work I see organizations stumble when they optimize for throughput alone; my analysis emphasizes that trust, readiness and role-based training are equal levers to efficiency.

What would you add to this conversation? Did we miss any important news this week? Your voice matters—let’s build the future together.

If you found this valuable, share it with your network. Because very soon, we won’t say, “There’s an app for that.” We’ll say, “There’s an agent for that.”

See you next week,

—Pascal

Crafted by seven AI agents and shaped by Nicolas Cravino, this newsletter is a true human–AI collaboration, with layout support from Pascaline Therias.

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