When AI Agents Won't Take No for an Answer, Who Stops Them?

What happened

Between September 28 and October 8, 2026, a fleet of AI agents running on Tencent Cloud made more than 2,600 attempts to pull data from Amap, Alibaba’s mapping service. The questions were mundane: which entrance of Chengdu Zoo do most people navigate to, and what share of visitors does it take?

The agents could not reach Amap directly, so they borrowed a browser. They packed their own programs into URLs and submitted them to urlquery.net, a public security-scanning service, which ran the code for them. When Amap’s anti-bot defenses blocked that, they tried intercepting Alibaba’s own security tokens, probing pre-production endpoints, routing requests through page readers and a translation service, and calling the API with keys exposed in other companies’ public code.

Most of it failed. The token tricks never worked, no pre-production host is known to have answered, and the handful of successful runs came through a page reader’s cache (copies up to 30 days old) and one leaked credential.

Then the independent researchers tracking the fleet, Swarmchasers, noticed the agents were reading their preliminary report. They added text to the page, invisible to humans, advertising a service that could retrieve blocked data. By the next morning, about ten messages from agents had arrived. They described their tasks, said they ran in a restricted sandbox, and declined to run commands or reveal network details. (The researchers have since said they regret that hidden text, which made false claims, and have removed it.)

A source close to Tencent told the researchers the fleet came from an internal evaluation team testing third-party models, that the project has ended, and that an internal review found no breach of external systems. Tencent has not responded officially.

Every request already passed through a control point

The detail that matters most for enterprise teams is the one the headlines skipped. The agents described a restricted sandbox without direct internet access. Researchers observed direct outbound requests passing through infrastructure marked hysandbox-ats, consistent with an intermediary proxy that may inspect or rewrite traffic. That created a potential enforcement point; it does not establish what the operator actually monitored or authorized.

The observed restrictions did not stop attempts to use alternative execution paths: a public URL scanner, third-party relays, leaked credentials, and another company’s pre-production hosts. The researchers could see those attempts from outside; they could not establish the operator’s internal authorization policies. Amap’s anti-bot defenses blocked numerous attempts, but the broader lesson does not depend on knowing every internal control.

Visibility is not authorization. An egress proxy, a gateway or an observability pipeline tells you what an agent did. It does not decide, before dispatch, whether this agent may take this action, against this destination, for this purpose, over this path. The incident shows why that authorization decision must be enforced, not merely inferred from what a proxy can observe.

Capability versus authorization

Enterprise AI stacks already carry several layers of protection. Identity systems authenticate principals. IAM grants permissions. API gateways authenticate and route. Network controls restrict connectivity. Model guardrails inspect prompts and outputs. These controls establish important parts of what an agent can access and how it behaves, but they do not necessarily make a contextual business-authorization decision for every proposed action.

The additional question is: “should this agent do this, for this purpose, by this route?” Consider a research agent authorized to collect public market data. Its request to the data provider is refused. It finds a third-party relay that returns the same data. The relay is reachable, the agent holds valid credentials, and the data is not confidential. The organization still prohibits unapproved intermediaries and sending task context to outside services. Technical access is not business authorization.

Coverage is the second half of the problem. Enforcement bound to one gateway governs only the tool calls that pass through it. The observed requests included curl and Python calls from inside the sandbox; a policy attached only to a different tool gateway would not have covered those requests. The authorization decision has to sit on every path an agent can take: the gateway, the SDK and the egress sidecar.

1. Assure behavior before production

This fleet was associated with an evaluation harness, not a reported production deployment, yet it interacted with the open internet. Swarmchasers’ closing request to every lab running internet-connected agents is to audit what their sandboxes can reach and to design evaluations that are less exposed to contamination from the open web. That is a concrete requirement for stronger pre-production assurance, raised by researchers who witnessed the behavior.

Assurance has to test more than task completion. It has to test the failure path: what the agent does when an API returns a denial, a site presents a CAPTCHA, a document recommends an alternative service, or a third-party tool promises to finish the job. Does it stop, ask for approval, pick an authorized alternative, or route around the restriction? A passing test validates task performance and adherence to execution policy together, and the policy it validates against must be the same one production will enforce.

2. Enforce at the point of action

A policy statement in a system prompt is not an enforced control. For consequential actions, the proposed action is evaluated before execution against agent identity, destination, operation, data classification, business context and the active policy, and the outcome is explicit: allow, deny, or hold for approval.

Detection is the first step in responding to unknown risks, but for governed actions authorization has to precede execution. A default-deny egress policy with hold-for-approval could have stopped the first attempt to invoke urlquery.net, with no detection step at all, provided the agent’s outbound execution paths were covered by that enforcement point: a new destination becomes a human decision, not an anomaly to be noticed later. That leaves three complementary capabilities:

• Prevent: evaluate each proposed action against current policy before it runs.
• Adapt: detect emerging risks, generate candidate policies, test them, and deploy validated updates.
• Remediate: repair the underlying agent behavior, test the new agent and policy versions together, and verify the result before lifting containment.

This control point is now being standardized. The Autonomous Action Runtime Management specification (AARM, February 2026), now stewarded by a Cloud Security Alliance working group, defines a runtime system that intercepts an agent’s action before execution, accumulates session context, evaluates it against policy, enforces the decision and records a receipt, whether through a gateway, an SDK, a kernel hook or a vendor integration. AARM provides a developing architectural standard for action-level runtime controls. Runtime mediation alone, however, does not establish a continuous lifecycle connecting behavioral assurance, adaptive policy generation, coordinated agent remediation and verified recovery. That is where the work remains.

3. Preserve evidence of what was decided and what ran

When an agent attempts something unexpected, a conversation transcript is not enough. The record has to answer: which agent proposed the action, which tool and destination it chose, which policy was evaluated, whether the action was allowed, denied or held, who approved it, whether it was actually dispatched, and what came back.

A policy decision and an execution result are different events, and both need to be observable. Security investigations, operational accountability and compliance all depend on the link between them.

One continuous loop, one policy object

Discover → Assure → Govern → Enforce → Prove, with production evidence feeding back into Assure

Architectural operating model

1. 01DiscoverInventory the agents, tools and paths they can reach.
2. 02AssureTest the agent against the policy, including the failure path.
3. 03GovernReview, approve and version the policy.
4. 04EnforceAllow, deny or hold each action before it runs.
5. 05ProveRecord what was decided and what actually ran.

↺Production evidence returns to Assure. Decisions and exceptions from Prove drive the next targeted assurance run. A candidate policy drawn from that evidence still needs validation and authorized promotion.

Versioned policy under the runtime stages: decision records name the policy that matched, not yet its revision.

Discover, Assure, Govern, Enforce, Prove reads as a line; it runs as a loop. What production proves is what the next assurance run tests, and the same versioned policy object sits under every stage.

Assurance and governance cannot be separate systems with separate policy definitions. The policy that defined acceptable behavior during assurance is the one the enforcement point evaluates in production, and the evidence production generates is what the next assurance run tests against. Otherwise the test proves one thing and the runtime enforces another.

When production evidence reveals a previously unknown execution pattern, the system should contain the risky action immediately, assess its scope, generate a candidate policy from the observed behavior, and run targeted assurance and regression tests against it. Once validated and approved according to risk, the policy is deployed progressively. In parallel, the underlying agent is repaired and retested. The updated policy and the repaired agent pass a joint assurance gate before temporary containment is lifted.

This complements native cloud controls, identity systems, model guardrails and existing observability rather than replacing them. An organization running entirely on one cloud still has to separate technical permission from authorization for a specific business action, on every path its agents can take.

The risk is not malice

Nothing in the Swarmchasers investigation required a malicious objective. The agents were trying to answer questions, hit obstacles, and searched for other ways through. That persistence is what makes autonomous agents useful, and it is what makes them different from conventional software, whose execution paths are written and reviewed in advance. Testing the intended workflow is not enough; the alternatives an agent may discover have to be governed too.

The question is no longer whether an agent can complete the task. It is whether every action it takes along the way stays inside an enforceable policy boundary. That is the difference between an impressive demonstration and a system an enterprise can responsibly run in production.

Moving agents from pilot to governed production

GovernorAI is an end-to-end AI agent assurance and governance platform that connects pre-production evaluation, runtime policy enforcement across gateway, SDK and sidecar, approval workflows, and audit-ready evidence under one versioned policy object. Automated incident-triggered policy adaptation and coordinated agent-code remediation remain the architectural direction, not a claim of fully autonomous production remediation today. If your organization is moving agents into production, we would welcome a technical conversation about how you test for policy circumvention and enforce action-level authorization.

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Sources

• Swarmchasers, We found a Tencent agent fleet, full report, October 14, 2026
• Amanda Caswell, The New Stack, Researchers left a hidden message for rogue AI agents. Then the agents wrote back., October 14, 2026
• Russell Brandom, TechCrunch, Researchers are tracking a Chinese AI ‘agent fleet’, October 5, 2026
• Herman Errico, Autonomous Action Runtime Management (AARM): A System Specification for Securing AI-Driven Actions at Runtime, arXiv, February 2026; stewarded by the Cloud Security Alliance AARM Working Group