AI Agents and the Agentic Web: What Businesses Must Do

By the time you finish reading this, an AI agent will have booked flights, negotiated a refund, repriced a product listing, and triaged a customer support backlog—without a human clicking through a single webpage.

Most companies still think of the internet as a human place: pages, forms, menus, and “add to cart” buttons. But researchers are increasingly serious about a different future: an agentic web, where autonomous AI agents become the primary users of online services and increasingly handle the work that humans used to do manually. IEEE Spectrum recently highlighted this shift through insights from UC Berkeley professor Dawn Song, a leading voice in AI safety and cybersecurity.

This matters for our Artificial Intelligence & Robotics: Transforming Industries Worldwide series because the agentic web isn’t just a software story. It’s the missing connective tissue between AI systems and physical automation—robots in warehouses, autonomous vehicles in smart cities, clinical workflows in hospitals. When the web becomes machine-to-machine by default, every industry that runs on transactions, scheduling, identity, and procurement feels the impact.

What the “agentic web” actually changes (and why it’s not just chatbots)

The direct answer: the agentic web shifts the internet from human interfaces to machine-to-machine negotiation and execution.

Dawn Song’s framing is simple: today’s web is designed around human limitations—limited attention, limited screen space, and linear browsing. If you want to buy a jacket, you search, scroll, compare, and decide. An agent doesn’t work like that. It can scan thousands of options, summarize tradeoffs, ask follow-up questions, and iterate instantly.

From webpages to “agent endpoints”

In an agentic web, the primary interface isn’t a page—it’s an interaction:

• Your agent expresses intent: “Waterproof jacket under $200, delivery before Jan 5, brand preference X, avoid PFAS coatings.”
• A retailer’s agent responds with options, inventory guarantees, substitution policies, and bundling offers.
• The agents negotiate constraints (delivery windows, return terms, loyalty credits) and execute payment.

Humans don’t disappear. But the default workflow becomes: human sets goals, agents do the legwork, human approves exceptions.

The internet economy becomes “API-first” again—at a new level

We’ve already lived through a version of this: businesses moved from brochure websites to apps and APIs. The agentic web is the next jump: APIs designed for reasoning, negotiation, and delegated authority, not just data retrieval.

That’s why the biggest winners won’t be the companies with the flashiest UI. They’ll be the companies with the clearest machine-readable policies: inventory truth, pricing logic, delivery SLAs, warranty rules, and identity assurance.

The protocols that will matter: identity, payments, and agent-to-agent trust

The direct answer: open protocols for agent tool use, agent communication, identity, and payments will define who can safely participate.

Song points to early examples of open protocols emerging for agent behavior:

• Tool-use protocols (how agents reliably call tools and services)
• Agent-to-agent (A2A) communication (how agents coordinate)

The next layer is where business leaders should pay attention: agent identity and agent payments.

Agent identity: “Who is this agent, and what is it allowed to do?”

Agent identity isn’t a nice-to-have. It’s the foundation for preventing fraud and limiting blast radius.

A practical way to think about it:

• An agent needs a verifiable identity (like a service account, but stronger)
• It needs scoped privileges (“can browse pricing,” “can place orders up to $500,” “cannot change bank details”)
• It needs an auditable trail: who delegated what, when, and with what constraints

Without identity and scoping, the agentic web turns every online workflow into a high-speed attack surface.

Agent payments: machines can transact faster than humans can notice

If agents can buy, book, bid, and subscribe on your behalf, payments become continuous and granular.

Expect a world where:

• Subscriptions shift to usage-based microcontracts
• Procurement becomes real-time bidding across suppliers
• Fraud evolves from “stolen card” to “stolen delegated authority”

For smart cities and industrial automation, this isn’t theoretical. Autonomous fleets will pay for charging, tolling, maintenance, and priority access. Warehouses will pay for parts, capacity, and rush shipping based on live demand.

Where agentic web meets robotics: industries that will feel it first

The direct answer: industries with high transaction volume, complex scheduling, and operational constraints will adopt agentic workflows fastest.

Here’s where I’d bet adoption shows up early, because the ROI is obvious and the workflows are already semi-structured.

Logistics and supply chain: autonomous procurement + autonomous movement

Logistics already runs on coordination: inventory, ETAs, dock schedules, carrier selection, customs paperwork.

With agents:

• A shipper’s agent can negotiate rates and capacity across carriers
• A warehouse agent can reorder fast-moving SKUs based on robotics telemetry (pick rates, errors, jams)
• A maintenance agent can schedule service before downtime hits

Pair this with robotics and you get a tight loop: robots generate operational signals; agents turn signals into transactions.

Healthcare: fewer clicks, more verification

Healthcare is drowning in portals, forms, authorizations, and scheduling. Agents can reduce the admin load—but only if identity and security are handled with care.

High-value agentic workflows include:

• Prior authorization assembly (collect evidence, code mapping, submission)
• Patient scheduling optimization (preferences, availability, travel time)
• Supply replenishment for clinical units

The catch: these agents will touch sensitive data. A single prompt-injection-style failure that leaks patient info is a regulatory and reputational disaster.

Smart cities: agent economies for infrastructure

Smart cities are moving toward sensor-driven operations: traffic control, energy balancing, parking, permitting.

The agentic web adds the missing market mechanisms:

• Agents negotiate curb access for deliveries
• EV charging agents reserve slots and pay dynamically
• City services allocate resources based on live demand (and enforce rules automatically)

This is where agent identity and payments stop being abstract protocols and become public infrastructure.

The uncomfortable part: AI agents multiply security risk

The direct answer: autonomous agents expand attack surfaces because they combine reasoning, tool access, and delegated authority.

Song’s warning is clear: we’re entering “uncharted territory.” LLMs already have known failure modes—prompt injection, data leakage, jailbreaks, tool misuse. Agents add new risks because they don’t just generate text; they take actions.

What changes when an AI can act?

Three concrete shifts:

1. Privilege becomes the payload: attackers won’t just steal data; they’ll steal what your agent is allowed to do.
2. Speed favors attackers: an agent can execute hundreds of steps quickly—so a compromise scales fast.
3. Multi-agent complexity hides failures: when agents call other agents, it becomes harder to trace blame and intent.

Real-world threat scenarios businesses should plan for

These are plausible, near-term issues—especially as agents browse the open web and interact with third-party tools:

• Prompt injection through “helpful content”: a malicious page or document includes instructions that trick the agent into revealing secrets or changing behavior.
• Data exfiltration through tool calls: an agent sends sensitive context to an untrusted service because the workflow wasn’t properly sandboxed.
• Transaction fraud via delegated spending: an agent is manipulated into purchasing from attacker-controlled vendors or changing payout details.
• Policy manipulation: an agent “negotiates” terms that violate company policy because the policy wasn’t encoded in enforceable constraints.

If your business is adopting AI agents in customer operations or procurement, the question isn’t whether you’ll see attacks. It’s whether you’ll notice them quickly and contain them.

How to prepare your company for the agentic web (practical steps)

The direct answer: prepare by treating AI agents like privileged software operators—governed, audited, and constrained by design.

You don’t need to wait for a fully realized agentic web to start. The same controls that will matter later are useful right now.

1) Design “least-privilege” agents, not general-purpose super-agents

Start with narrow scopes:

• One agent for customer support refunds (with strict limits)
• One for travel booking (no access to payroll or vendor banking)
• One for inventory reorder suggestions (human approval required)

If an agent can do everything, it can fail everywhere.

2) Separate reasoning from execution

A pattern I’ve found effective: let the agent propose actions, but require a hardened execution layer to enforce rules.

• Agent drafts the plan
• Policy engine validates constraints (budget, vendors, geography, compliance)
• Execution service performs the action with auditable logs

This reduces the risk that a clever prompt turns into an irreversible transaction.

3) Build an “agent audit trail” you can defend

If an agent places an order or changes a workflow, you’ll want answers in minutes:

• What inputs did it see?
• Which tools did it call?
• What identity did it present to third parties?
• What policy checks were applied?

Assume auditors (or customers) will ask.

4) Treat third-party agent interactions like third-party code

In an agentic web, your agent will interact with external agents and services. That’s the same risk class as adding a new dependency.

Operational guardrails:

• Allowlists for trusted services
• Sandboxed browsing environments
• Rate limits and spending limits
• Continuous monitoring for unusual behavior

5) Invest in red teaming that targets agent behavior

Song mentions multi-agent red teaming approaches—using agents to attack agents. That’s directionally right.

Your testing should include:

• Prompt injection attempts across realistic web content
• Tool misuse simulations (wrong vendor, wrong recipient, wrong permissions)
• Data leakage probes (can it reveal secrets in edge cases?)
• “Benign failure” tests (how it behaves when confused or missing data)

If you only test accuracy, you’re grading the wrong subject.

What this means for the next 12–24 months

The direct answer: you’ll see hybrid web experiences where humans approve and agents execute, and businesses will compete on trust and machine-readability.

The agentic web won’t arrive as a single switch flip. It’ll show up as a blend: agent-friendly service layers behind familiar apps and websites. Some companies will expose structured “agent endpoints” first for customer operations and procurement because the efficiency gains are immediate.

But security will decide the pace. Organizations that treat agents as privileged operators—complete with identity, scoped permissions, auditable execution, and adversarial testing—will move faster with fewer embarrassing incidents.

For the broader AI and robotics story, this is a big deal: automation inside the factory or hospital only scales when procurement, scheduling, compliance, and payments keep up. The agentic web is where that coordination happens.

If you’re building or buying AI agents now, the question to ask your team isn’t “Can it do the task?” It’s: “Can it do the task safely, repeatedly, and with proof?”

What would your business look like if, by next holiday season, most of your online transactions were negotiated by machines rather than clicked by people?