--- title: "Autonomous AI Agent Control | Firevault" url: https://fire-vault.com/learn/autonomous-ai-agent-control description: "Control autonomous AI agent connections with accountable authority, physical intervention, verified path states and safe restoration. CP-18 AI Connectivity…" lang: en-GB --- Control by Firevault · CP-18 AI Connectivity Control # Autonomous AI agent control. Govern what agents can reach. An agent given tools can act beyond the conversation. Define its permitted connections, who can withdraw them and what must be checked before work resumes. Mark Fermor · CTO, CMO & Founder · 31 October 2026 ## When a tool call becomes a business action. A research agent reads a supplier document containing hostile instructions. With access to internal tools, it may attempt an action outside the agreed task. The important question is not only what the model says, but what the surrounding system allows it to do. **Control by Firevault governs authorised changes to selected physical connections.** It complements agent permissions, identity, monitoring and incident response. It does not assess the truth of model output or provide an AI safety filter. The relevant Control Plans are CP-18 AI Connectivity Control (https://fire-vault.com/control-plan/control-ai-systems) and CP-20 Autonomous System Intervention (https://fire-vault.com/control-plan/autonomous-system-intervention); Execute (https://fire-vault.com/control-module/execute) is the Module most often involved. The separate AI Blueprint is BP-15 (https://fire-vault.com/control-blueprint/ai). This guide adds autonomous-agent context without creating a competing Plan. ## A public architecture of responsibility. Start with one consequential connection, not a promise to stop every agent. These are design responsibilities, not a wiring diagram or an approved deployment specification. ### Task and tool authority The AI owner defines the task, data scope and tools. Use narrowly scoped permissions and approval for consequential actions. A physical boundary does not replace application authorisation. ### Selected connection scope The network owner identifies the connection proposed for intervention and assesses parallel routes, shared services, cloud tools and dependencies. Unassessed routes remain outside the intervention. ### Intervention authority A named operator or configured external control authorises the state change. Detection supplies evidence; the agreed operating procedure determines whether intervention is appropriate. ### Evidence and restoration Record the request, reported device state and independent verification separately. The service owner assesses queued actions, continuity and safe restoration before reconnecting. ## Requesting a cut is not proving a cut. Illustrative operating states for one selected connection. This is not live device telemetry and does not prescribe a validated installation. ### Authorised operation The agent may use the selected connection within its approved task. Tool permissions, identity checks and monitoring remain necessary. Selected path Available or not yet verified closed Independent verification Not established by this state Model and local computation Not stopped by this path change No guaranteed switching time is claimed. Command delivery, dependencies and failure behaviour require deployment-specific testing. ## Governance gives the boundary its purpose. ### NIST: manage risk across the lifecycle The AI RMF (https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf) organises voluntary risk management around Govern, Map, Measure and Manage. For this use case, establish accountable owners, map the agent environment, measure behaviour and manage the response. The Generative AI Profile (https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf) extends that resource for generative AI. It is a governance reference, not proof that a particular Control deployment satisfies a standard. NIST describes agent hijacking through indirect prompt injection (https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations). Test hostile instructions in retrieved content as well as ordinary task performance. ### Anthropic: retain control as autonomy grows Building effective agents (https://www.anthropic.com/research/building-effective-agents) distinguishes predefined workflows from agents directing their own process and tool use. Start with the simplest suitable approach and introduce autonomy only where justified. Its safe and trustworthy agents framework (https://www.anthropic.com/news/our-framework-for-developing-safe-and-trustworthy-agents) offers principles for responsible development. Defined intervention authority is one practical design consideration alongside those broader principles. Trustworthy agents in practice (https://www.anthropic.com/research/trustworthy-agents) discusses human control, prompt injection and secure interactions. Review tool exposure and the limits of oversight, not just the model response. **Neither NIST nor Anthropic endorses Firevault here or requires this physical-control design.** The mapping above is Firevault commentary on their published guidance. ## What must be agreed before deployment. Control is delivered on your premises. AI owners, network architects, service owners and incident operators need a shared acceptance record for the proposed intervention. ### Scope and accountability - Which task and selected connection are in scope? - Who may authorise intervention and restoration? - Which alternate routes, cached data and shared services remain? ### Continuity and evidence - What happens to in-flight and queued actions? - How are requested, reported and verified states distinguished? - What happens if a command is delayed or cannot be delivered? - How is safe restoration tested and documented? Disconnecting a selected path does not stop a model, local computation or an action already completed. It does not guarantee containment across the estate. Safety-critical dependencies and alternate communications need specific review before interruption is authorised. ## Source record. 1. NIST: AI Risk Management Framework 1.0 (https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf) Voluntary risk-management functions: Govern, Map, Measure and Manage. 2. NIST: Generative AI Profile (https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf) A cross-sector companion to the AI RMF addressing generative AI risks. 3. NIST: Strengthening AI Agent Hijacking Evaluations (https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations) Indirect prompt injection and evaluation of unintended agent actions. 4. Anthropic: Building effective agents (https://www.anthropic.com/research/building-effective-agents) Distinguishes predefined workflows from agents directing their own processes and tool use. 5. Anthropic: Framework for developing safe and trustworthy agents (https://www.anthropic.com/news/our-framework-for-developing-safe-and-trustworthy-agents) Principles for responsible agent development, not a product certification. 6. Anthropic: Trustworthy agents in practice (https://www.anthropic.com/research/trustworthy-agents) Human control, prompt injection and secure agent interactions. ## Start with one consequential connection. Bring the agent task, tool access and service dependencies. Define an intervention that your accountable owners can authorise, test and restore. Book a briefing (https://fire-vault.com/contact?subject=autonomous-ai-agent-control) Get started ## Structured data ```json [ { "@context": "https://schema.org", "@type": "WebPage", "@id": "https://fire-vault.com/learn/autonomous-ai-agent-control#webpage", "url": "https://fire-vault.com/learn/autonomous-ai-agent-control", "name": "Autonomous AI Agent Control", "description": "Control autonomous AI agent connections with accountable authority, physical intervention, verified path states and safe restoration. 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