Agentic AI: From Hype to Hard Choices – How Enterprises Should Pick Use Cases That Last

by user | Oct 17, 2025

Introduction

Agentic AI – autonomous, goal-driven systems that can carry out multi-step tasks with minimal human prompting – has gone from proof-of-concept demos to a flood of vendor product launches and enterprise pilots. But hype has outpaced demonstrated value: recent analyst research warns that supply of agentic solutions exceeds demand and that a large share of current projects will be canceled before delivering sustained impact.

This post explains why many agent projects stumble, which types of use cases are most likely to survive the coming shakeout, and practical steps product and engineering leaders should take to build resilient, valuable agentic systems.

Why so many agent projects fail

There are three recurring failure modes I’ve seen across enterprises and vendors:

• Misaligned expectations: Agents are framed as magical productivity multipliers but are often introduced without clear goals, success metrics, or executive sponsorship.
• Underestimating systems work: Effective agents need reliable data, connectors, monitoring, and safety guardrails – the real effort is systems engineering, not model selection.
• Risk and compliance gaps: Agents acting autonomously can surface legal, privacy, or brand risks. Without strong policies and observability, organizations pause or cancel projects.

Analyst signals reflect this reality. Gartner estimates that many agentic AI projects will be canceled in the next few years; at the same time, a meaningful minority of enterprise applications will include agents by the decade’s end. That divergence points to a selective future: not every agent will survive, but the ones that solve well-scoped, high-value problems will thrive.

Use cases that are likely to survive

Focus on areas where agents can reduce clear operational cost, speed decision loops, or unlock new revenue without creating outsized risk. Examples:

• Repetitive knowledge work with structured inputs: invoice processing, triaging standard support tickets, or summarizing compliance documents where outcomes are verifiable.
• Assistant layers that stitch existing systems: agents that orchestrate CRM, ERP, and marketing systems to automate common workflows (e.g., opportunity-to-quote) while keeping humans in the approval loop.
• Compliance-first automation: monitored agents that surface exceptions and provide audit trails, rather than fully autonomous decision-makers where legal liabilities are high.
• Developer and ops assistants: curated agentic tooling that accelerates coding, testing, or incident remediation with guardrails and rollbacks.

These winners share three properties: measurable ROI, constrained action space, and easy-to-verify outputs.

How to design agentic solutions that last

If you’re evaluating or building an agentic product, center your approach on systems, not models. Key principles:

1. Define the business metric first

Start with the metric you care about (handle time, time-to-revenue, cost-per-case) and frame the agent as an experiment to move that metric. Avoid launching agents as feature demos.

1. Constrain the agent’s action surface

Limit the APIs, data, and write privileges an agent can access. A smaller action surface reduces risk and makes behavior predictable.

1. Build observability and audit trails

Log agent decisions, inputs, and downstream effects. Observability enables debugging, compliance checks, and continuous improvement.

1. Invest in data plumbing and integration

Reliable connectors, canonical data views, and retry semantics are what make agents robust in production. This is often the majority of the engineering work.

1. Layer guardrails and human oversight

Design for human-in-the-loop escalation on exceptions and approvals for high-risk actions. Automated rollback and “safe mode” are critical deployment features.

1. Measure the cost to operate

Track not just model inference cost but human review time, integration maintenance, and incident handling. A low headline automation rate can still be valuable if it reduces specialized labor costs.

1. Plan for governance and lifecycle management

Define versioning, access controls, performance SLAs, and a deprecation strategy – agentic features will be subject to consolidation and regulation.

Product and GTM implications

• Sales teams: sell results, not agents. Position agentic features as workflow improvements with clear KPIs.
• Engineering: prioritize integration, monitoring, and SRE practices for agents; automate testing that covers end-to-end task flows.
• Legal/Compliance: involve privacy and risk teams early. Build templates for approvals and incident response.
• Vendors: focus on composability – customers will prefer modular “skills” they can assemble safely rather than monolithic autonomous agents.

Conclusion

Agentic AI is real and will reshape parts of enterprise software, but the early market is noisy. Many projects will be canceled not because agents are inherently flawed, but because initiatives lacked clear metrics, system-level engineering, or governance. The winners will be the organizations and vendors that treat agents as integrated systems: constrained, observable, and designed around measurable business outcomes.

Key Takeaways
– Agentic AI supply currently outpaces real-world demand; prioritize high-value, low-risk use cases.
– Design for data, guardrails, and composability – success depends on systems, not just models.

This Week in AI: Skills, Agents, and the New Rules of Engagement

by user | Oct 16, 2025

Introduction

This week brought another fast burst of product launches, policy moves, and infra signals that together map where practical AI is headed next. The pattern is clear: vendors are packaging agent capabilities into reusable modules and platforms while regulation and authentication efforts are racing to keep up. At the same time, chipmakers and cloud players continue to push infrastructure costs and supply decisions onto product roadmaps.

This week in AI: agents, chips, and rules

Below are the standout developments product teams, security leads, and executives should care about.

• Anthropic’s “Skills” for Claude
• What happened: Anthropic introduced “Skills,” a way for companies to bundle custom instructions, connectors, and resources for Claude across its app, API, and Agent SDK. Early partners include Box, Rakuten, and Canva.

• Why it matters: Skills formalize a modular layer between raw prompts and full apps – letting organizations reuse, version, and govern capabilities across teams and channels.

• Salesforce launches Agentforce 360

• What happened: Salesforce unveiled a suite for building enterprise agents (Agent Script policy format, a reasoning engine, Slack and voice integrations).

• Why it matters: Expect more vendors to offer policy-as-code and agent orchestration primitives, which simplifies compliance but raises questions about standard formats and portability.

• Visa’s Trusted Agent Protocol

• What happened: Visa, with partners like Cloudflare and Microsoft, proposed a protocol to authenticate shopping agents and distinguish them from malicious bots ahead of the holidays.

• Why it matters: Commerce will need identity and provenance for agent-driven checkouts – a new layer between payment rails and AI clients.

• California’s AI disclosure law

• What happened: California passed a law requiring some chatbots to disclose they’re AI and mandating safety reporting for mental-health interactions beginning in 2026.

• Why it matters: Product and legal teams must bake clear, usable disclosures and logging into UX flows, especially for consumer-facing and health-related agents.

• Chip and infra signals: TSMC, Nvidia, and CAPEX

• What happened: TSMC lifted outlook on AI demand, helping Nvidia and other chip suppliers; infrastructure spending remains strong.

• Why it matters: Teams should budget for higher inference and fine-tuning costs and plan for potential capacity constraints or vendor lock-in.

• Research and commerce nudges

• MIT published methods to help VLMs find personalized objects in scenes (useful for AR/robotics), and Adobe forecasts a huge jump in AI-assisted holiday shopping.
• Why it matters: Personalization capabilities will accelerate product opportunities – and regulatory + privacy trade-offs will follow.

What this means for product, security, and legal teams

• Build modularly: Treat “skills” and agent components as first-class artifacts – version, test, and monitor them the way you do microservices.
• Plan for governance: Adopt policy-as-code and auditing hooks now. Log decisions, keep human-in-the-loop checkpoints for sensitive domains, and prepare compliance docs for disclosures required by new laws.
• Prepare an identity layer: Work with payments and identity partners to support agent attestation and provenance for commerce scenarios.
• Revisit cost models: Infra CAPEX and per-inference pricing will influence model choice and latency budgets – run small-scale cost projections for anticipated holiday traffic.
• Watch research-to-product paths: Techniques that localize a user’s personal objects or enable agent chaining will unlock features, but guardrails and privacy-preserving defaults are essential.

Conclusion

The common thread this week is maturation: agentic AI is moving from research demos and prompts into modular, governed platforms that enterprises can deploy. That’s great for capability and speed – but it also raises immediate questions about cost, identity, and compliance. Product leaders who adopt modular design, policy-as-code, and agent identity standards early will be best positioned to capture value and reduce risk as agentic interactions scale.

Key Takeaways
– Agent platforms are moving from prompts to modular ‘skills’ and policy-as-code, making enterprise agents easier to build and govern.
– Regulation, identity layers for agents, and continued chip-driven infra spending mean product and legal teams must plan for compliance, authentication, and higher AI costs.

The Trusted Agent Protocol and the Rise of Agentic Commerce: What Merchants Need to Know

by user | Oct 15, 2025

Introduction

Visa’s recent announcement of a Trusted Agent Protocol marks a practical turning point in how commerce platforms will handle the coming wave of agentic AI – autonomous shopping assistants that can discover, negotiate, and transact on behalf of customers. With partners like Microsoft, Shopify, and Adyen involved, the protocol aims to let merchants verify legitimate AI agents and separate them from malicious bots. This matters because the 2025 holiday season could be the first mass test of AI agents shopping and checking out at scale.

This post explains what the protocol does, why it matters, and concrete steps merchants and payments teams should take now to prepare.

What is the Trusted Agent Protocol?

In short: a set of standards and identity signals that let merchants and payment processors recognize authenticated AI shopping agents. Rather than try to ban bots outright, the protocol creates a trust layer so services can:

• Identify an agent’s provenance (who built it and who vouches for it).
• Verify that the agent follows merchant rules (pricing, promotions, and checkout limits).
• Distinguish human-initiated sessions from autonomous agents to enable different UX and risk controls.

The goal is pragmatic: keep legitimate agent-driven commerce flowing while blocking fraud, scalping, and abusive automation.

Why this matters now

1) Agentic commerce is arriving fast. Pilots in payments and conversational checkout (including India’s UPI experiments linking agents to payment rails) show the path to real purchases inside chat assistants.

2) Without identity and trust signals, merchants face new attack surfaces: credential stuffing, scalping agents participating in flash sales, and automated cart manipulation.

3) Regulations are beginning to land. California’s new chatbot disclosure law and other state or national initiatives are likely to change how assistants must present themselves and report safety data.

4) Holiday season timing. The protocol’s rollout ahead of the holidays signals urgency – fraud exposure and poor UX during peak shopping could be costly.

How merchants should prepare (practical checklist)

• Map agent touchpoints: catalog search, price/discount eligibility, inventory checks, and checkout flows.
• Add agent-aware policies: create separate rate-limits, quotas, and pricing eligibility checks for authenticated agents versus anonymous clients.
• Integrate identity signals: accept and validate Trusted Agent tokens from supported identity providers; log provenance metadata for audits.
• Harden checkout fraud controls: require additional verification for high-value purchases initiated by agents (e.g., step-up authentication, delayed fulfillment rules).
• Align marketing & promotions: decide whether agent-driven purchases qualify for specific coupons, bundles, or loyalty multipliers.
• Update Terms of Service and privacy notices: disclose how agent-originated data is used and retained.

Payments, partnerships, and pilots

Payment networks and fintechs will be central to agentic commerce. Early pilots (including collaborations tying conversational agents to UPI in India) show that integrating agents with payment rails is technically feasible and commercially attractive. For merchants, this means engaging payment partners now to ensure proper tokenization, dispute flows, and liability rules are in place for agent-initiated transactions.

Policy and risk considerations

• Consumer transparency: laws like California’s SB 243 require disclosure that a user is interacting with an AI. Design UX to make agent identity obvious and consent explicit.
• Fraud vs. convenience trade-off: overly strict blocks risk degrading user experience; lax rules invite abuse. The Trusted Agent Protocol helps strike a balance but doesn’t remove the need for merchant-level controls.
• Market dynamics: IMF commentary suggests the AI investment cycle may see corrections; still, innovation in commerce is likely to continue and fragment across vendors – meaning merchant interoperability matters.

Recommendations for leadership

• CTO/Head of Engineering: prioritize agent token validation and telemetry. Ensure downstream systems record agent provenance for analytics and disputes.
• Head of Payments/Fraud: re-evaluate risk scoring to include agent-origin signals and create agent-aware exception rules.
• Product/UX: design clear agent disclosure patterns and friction points where human confirmation is required.
• Legal/Compliance: monitor emerging state and national rules; update policies and reporting pipelines as needed.

Conclusion

Visa’s Trusted Agent Protocol is the industry’s first serious attempt to give agentic commerce a trust fabric. For merchants, the choice is simple: prepare, integrate, and design with agent identity in mind – or risk being surprised when autonomous assistants scale up during peak shopping periods. The protocol won’t solve every fraud or policy problem, but it gives businesses the tools to separate legitimate assistants from bad actors and to design safer, more predictable agent-driven experiences.

Key Takeaways
– Visa’s Trusted Agent Protocol creates a standard for authenticating AI shopping agents, helping merchants distinguish legitimate assistants from bad bots.
– Merchants, payment providers, and regulators must adapt UX, identity, and risk controls now to safely enable agent-driven commerce before the holidays.

California’s New AI Disclosure Law and the Microsoft–OpenAI Antitrust Suit: What’s Next for AI Policy and Competition

by user | Oct 14, 2025

Introduction

This week brought two high-impact developments for generative AI: California enacted a consumer-facing law requiring chatbots to disclose that they are AI, and a group of consumers filed a class-action antitrust suit alleging Microsoft’s partnership with OpenAI has distorted competition for compute and AI services. Taken together, these moves mark a shift from speculative discussion about AI harms to concrete legal and regulatory actions that will affect product design, commercial contracts, and platform economics.

In this post I summarize what each action requires or alleges, explain why they matter beyond the headlines, and offer practical steps for companies, developers, and users preparing for a more regulated and legally contested AI landscape.

California’s AI disclosure law: what it does and why it matters

Summary

• The new California law requires consumer-facing chatbots to clearly identify themselves as artificial intelligence when interacting with people. In addition, for certain safety-sensitive scenarios – such as content involving self-harm – operators may face reporting obligations tied to public safety offices.

Why this matters

• User trust and UX: Clear disclosure changes conversational UX. Product teams must design disclosure flows that are honest but not disruptive: consider introductions, tooltips, and privacy screens.
• Compliance and enforcement: States can move faster than federal law. Multiple states adopting similar rules would create a patchwork that larger platforms will need to track and comply with.
• Safety processes: New reporting obligations (even if limited) push operators to formalize incident handling, logging, and escalation pathways for sensitive outputs.

Practical steps for product and legal teams

• Audit all consumer chat interfaces to ensure a clear, persistent disclosure that the user is speaking to AI; avoid hiding the disclosure in dense terms or deep settings.
• Document content moderation and safety processes, including logs and escalation rules for self-harm or violence scenarios to satisfy potential reporting requirements.
• Review onboarding flows, API partners, and third-party agents to ensure the disclosure applies across embedded experiences.

The Microsoft–OpenAI antitrust suit: the allegation and implications

Summary

• A class-action filed in federal court alleges Microsoft’s exclusive or preferential arrangements with OpenAI restricted access to compute resources, raised prices for downstream services like ChatGPT, and harmed competition in cloud and AI markets.

Why this matters

• Compute as a chokepoint: The complaint frames high-performance compute and specialized hardware as bottlenecks that confer market power, a novel angle in antitrust litigation for AI.
• Contract transparency and exclusivity: If the courts find that exclusive arrangements meaningfully foreclose competition, companies may face limits on how they structure commercial deals with AI startups or cloud providers.
• Broader market consequences: Rulings could change pricing models for hosted AI services or encourage more open, interoperable compute marketplaces.

Practical steps for vendors and partners

• Reexamine commercial contracts for exclusivity clauses, capacity reservations, and favorable pricing that could be litigated as anti-competitive.
• Preserve documentation showing procompetitive justifications (e.g., joint investments, performance improvements, consumer benefits) to rebut claims that partnerships harmed competition.
• Consider diversification strategies: avoid single-supplier dependencies for critical compute resources where feasible.

How these two trends fit together

The disclosure law and the antitrust suit reflect complementary regulatory pressures:

• Transparency and safety rules are being used to protect users directly – forcing product-level changes and accountability.
• Antitrust actions aim to protect market structure and access – targeting the economics that determine who can build and scale AI services.

For companies, this means preparing on two fronts: compliance and design to meet user-facing transparency and safety requirements, and commercial/legal defenses to manage competition and contract risks.

Conclusion

We’re moving out of the era of “AI as an abstract future problem” into one where states and courts are issuing concrete requirements and tests. The immediate effects will be practical: label your bots, document safety handling, review commercial deals, and reduce single-point dependencies. Longer term, expect more legislative experimentation, cross-jurisdictional rules, and litigation that reframes how compute, data, and model access are governed.

If you build or operate conversational AI, start with a small compliance checklist today: add a clear disclosure to all consumer-facing dialogs, inventory your safety reporting processes, and audit compute and partnership contracts. These simple steps will reduce legal risk and build user trust as the regulatory and legal environment evolves.

Key Takeaways
– California’s new disclosure law requires chatbots to tell users they’re talking to AI and creates new safety reporting obligations.
– A consumer antitrust lawsuit alleges Microsoft’s OpenAI deal restricts compute access and harms competition; the case could reshape AI platform economics.
– These moves accelerate both product-level transparency/safety work and legal scrutiny of how AI infrastructure and partnerships are structured.
– Companies should implement immediate UX and compliance changes while auditing commercial contracts and supplier dependencies.

Key Takeaways
– California’s new disclosure law requires consumer-facing chatbots to clearly identify themselves as AI and adds reporting obligations for certain safety scenarios, raising compliance and UX questions.
– A consumer class-action antitrust suit against Microsoft alleges the OpenAI partnership restricts compute access and harms competition – a case that could reshape how cloud compute and AI partnerships are regulated.

Agentic AI for Enterprises: What Leaders Need to Know Now

by user | Oct 13, 2025

Introduction

Agentic AI – systems that act autonomously to complete multi-step tasks (often called “agents”) – have graduated from research demos to commercial products. Major cloud providers are packaging agent capabilities for businesses, regulators are racing to keep up, and policymakers are shaping the conditions under which firms can deploy these systems at scale. For product leaders and executives, the question is no longer whether agentic AI matters, but how to adopt it responsibly and capture measurable value.

Why agentic AI matters for enterprises

Agentic AIs extend large language models by combining planning, external tool use (APIs, browser automation), and multi-step execution. That shift unlocks use cases beyond single-turn chat:

• End-to-end automation of routine processes (e.g., invoice intake → classification → reconciliation).
• Augmented knowledge workers (research assistants that gather, synthesize, and draft proposals).
• Customer support agents that autonomously triage, resolve, or escalate issues.

Recent product moves illustrate momentum: Google announced Gemini Enterprise to bring agent features to business customers, and new “computer use” models can interact with web apps and UIs directly. These advances lower friction for automating real workflows – but they also increase dependence on integration, monitoring, and guardrails.

Where enterprises are actually seeing ROI (and where they aren’t)

High-value, high-confidence wins first:

• Repetitive, rules-based processes with measurable KPIs (e.g., claims processing, order entry).
• Knowledge aggregation and first-draft generation where human review is quick and inexpensive.
• Orchestration tasks that stitch together existing systems (calendar, CRM, ticketing) with predictable outcomes.

Harder bets that often under-deliver:

• Complex judgment tasks requiring deep domain expertise or legal liability.
• Broad, unsupervised agents tackling fuzzy goals without clear success metrics.
• Large-scale replacements of customer-facing decision points without phased testing.

The practical lesson: pilot narrowly, measure tightly, and scale only after you prove value and safety.

Regulatory and policy landscape – what to watch

The policy environment is active and fragmented:

• Regional industrial strategies (e.g., EU “Apply AI” plans) are accelerating adoption but also promote local compliance frameworks and sovereignty requirements.
• National-level export controls or “full-stack AI export” initiatives can affect where you host models or move data.
• Subnational rules (state procurement policies, sector-specific pilots) can create a patchwork of requirements for companies operating across jurisdictions.

That means architecture choices matter: data residency, model provenance, audit logs, and human-in-the-loop controls should be design-first decisions, not afterthoughts.

Implementation checklist for execs and product leaders

1. Start with a mission-specific pilot
2. Define a narrow, measurable objective and a baseline for comparison.
3. Inventory data and integrations
4. Map data sensitivity, PII exposure, and downstream systems before connecting an agent.
5. Build governance and monitoring
6. Logging, drift detection, and human review points for any decision with risk.
7. Choose the right deployment model
8. On-premise, cloud provider-managed, or hybrid – weigh latency, compliance, and control.
9. Measure safety and business metrics together
10. Track accuracy, time saved, error rate, and customer satisfaction in parallel.
11. Plan for incremental escalation
12. Start with assistive agents, then move to semi-autonomous and (only if safe) fully autonomous workflows.

Conclusion

Agentic AI is a practical tool for automating and augmenting work, not a magic bullet. The companies that win will be those that pair targeted pilots with strong data governance, measurable KPIs, and an eye on regulatory constraints. Treat agent deployments like product launches: small experiments, clear metrics, staged rollouts, and operational controls.

Key Takeaways
– Agentic AI can automate complex workflows and free human time, but ROI is uneven – start with targeted, high-value pilots and clear measurement.
– Regulation, export controls, and privacy rules are shaping deployments; build compliance, data governance, and human-in-the-loop controls from day one.