As companies move from experimenting with AI agents to deploying them in production, one pattern becomes clear: capability without control is a liability. Agents operate in long-running, stateful environments. They browse the web, read repositories, execute shell commands, call APIs and interact with internal systems. That power is transformative — and it meaningfully expands the attack surface . In a recent interview, Jonathan Wall, CEO of Runloop, summarized the shift: “By default, agents should have access to very little. They need to do real work, but capabilities have to be layered on in a controlled way.” That framing reflects a broader industry reality: agent infrastructure must be designed around least privilege, explicit isolation and observable execution. What follows is a practical control architecture for production agents. The layered control model A resilient agent deployment combines six explicit layers: Strong runtime isolation with a microVM Restrictive network policy with explicit egress allowlists Centralized credential management through a gateway Disciplined identity management with short-lived, scoped credentials Deliberate friction around sensitive actions and high-risk tools Continuous monitoring, logging and adversarial testing Each layer addresses a different failure mode. Together, they contain blast radius when — not if — something breaks. Start with least privilege A production-grade agent environment begins in a constrained state: Isolated runtime boundary, no inbound access, no outbound network access and no implicit tool permissions. The runtime boundary itself is part of least privilege. Containers provide efficient isolation for trusted or single-tenant workloads, but they share a host kernel. Real-world escape vulnerabilities have repeatedly shown that this boundary can fail under adversarial pressure. CVE-2019-5736 allowed attackers to overwrite the host runc binary from within a container ; CVE-2022-0492 enabled breakout via cgroups misconfiguration; CVE-2024-21626 again exposed runc-based escape paths. These incidents do not render containers unusable — but they clarify the tradeoff. MicroVMs introduce a stronger hardware-level boundary, reducing blast radius when agents execute arbitrary or unvetted code. Isolation is not a performance decision alone. It is a risk decision. The modern agent threat model Traditional SaaS systems process deterministic requests. Agent systems ingest untrusted content and generate probabilistic actions. Prompt injection has demonstrated how fragile instruction boundaries can be. In 2023, public experiments against Bing Chat showed that hidden instructions embedded in web pages could override system prompts. Academic research from Stanford and others has shown that tool-using agents can be coerced to leak credentials or proprietary data when external content is treated as trusted context. The danger compounds when agents operate with broad credentials. Service accounts, long-lived API keys and shared internal tokens convert a successful injection from “unexpected output” into repository compromise, database access or SaaS abuse. System prompts that embed internal URLs or configuration data become reusable artifacts once exposed. Retrieval-augmented systems and MCP-style integrations widen the surface further. When external documents are ingested without segmentation or role separation, attacker-controlled content can redirect behavior or induce data disclosure. This is the environment the layered model must withstand. Network policy as containment Network controls are often treated as compliance checkboxes. In agent systems, they are containment mechanisms. Agents typically require outbound access for documentation lookup, dependency installation or API interaction. Yet unrestricted egress provides the cleanest path for data exfiltration after injection. Restrictive allowlists — permitting only explicitly approved domains or endpoints — dramatically reduce blast radius. If a model is tricked into reading a .env file, a strict egress policy can prevent the obvious next step: shipping those secrets to an attacker-controlled domain. Logging outbound traffic establishes behavioral baselines and highlights anomalies early. Containment turns catastrophic compromise into a recoverable incident. Ingress as an operational event Most agent runtimes do not require unsolicited inbound connections. Leaving services exposed by default accumulates unnecessary risk. When debugging or collaborative inspection is required, exposure should be temporary and scoped — authenticated tunnels opened deliberately and closed promptly. Ingress becomes an operational decision rather than a static configuration state. Ephemerality is a security control. Governing model access Large language models are external systems with cost, compliance and leakage implications. Allowing each runtime to independently manage model credentials fragments oversight. A centralized gateway restores control. It can restrict approved models, enforce rate ceilings, log prompts and responses, and apply filtering or compliance checks. Agents no longer hold raw provider credentials directly. The lesson from both container escapes and prompt injection incidents is consistent: implicit trust boundaries erode. Centralized governance reinforces them. Tooling, identity and friction by design As agents integrate with repositories, CI systems, deployment pipelines and databases, tool governance becomes inseparable from identity discipline. Dedicated identities per agent, short-lived tokens and strict RBAC or ABAC reduce the impact of compromise. Reusing human or root-level credentials collapses isolation entirely. Sensitive actions — sending email, modifying production code, accessing secrets, changing authentication — benefit from friction. Policy checks, approval workflows or out-of-band confirmations create deliberate pauses at high-risk boundaries. Secrets should not live in prompts. System prompts embedded with credentials have been shown to leak under injection pressure. External secret managers and strict separation between model-visible text and credential material materially reduce exposure. Continuous adversarial testing Container escape CVEs and public prompt injection demonstrations share a common lesson: systems fail at integration boundaries, not in isolation. Logging tool calls, data access and network egress creates behavioral baselines against which anomalies — unusual domains, atypical file reads, unexpected tool invocation patterns — can be detected early. Red-teaming and adversarial prompt fuzzing help surface injection paths before attackers do, forcing organizations to confront weaknesses under controlled conditions rather than in production. Agents can build, test, browse and execute arbitrary code. That capability is powerful — and dangerous when unconstrained. Production readiness is therefore defined not by what agents can do, but by how precisely their boundaries are defined, enforced and observed. The organizations that scale agents successfully will treat infrastructure as policy, isolation as a design decision and monitoring as a first-class requirement — not an afterthought. This article is published as part of the Foundry Expert Contributor Network. Want to join?