AI Agent Discovery: How to Find & Secure AI Agents Across Your Enterprise

AI Agent Discovery: How to Find and Govern AI Agents Across Your Enterprise

The enterprise mandate has violently shifted. Organizations spent recent cycles rushing to build autonomous pipelines, chaining Large Language Models (LLMs) to corporate infrastructure, SaaS backends, and operational data stores. In production, engineering leads, platform architects, and security officers face an uncomfortable operational reality: enterprises do not know how many autonomous agents are running in their environment right now.

Teams deploy internal frameworks like CrewAI, LangGraph, and cloud native services on AWS Bedrock, Google Cloud Vertex AI, and Microsoft Azure Foundry. At the same time, traditional enterprise SaaS platforms quietly push routine patches that turn dormant, deterministic code paths into semi-autonomous, agentic actors.

This is the shadow agent explosion. Traditional IT discovery and static security controls are blind to it.

Enterprises have spent millions securing model weights, scrubbing training sets, and configuring prompt guardrails. Yet prompt engineering and static boundary filters do not prevent a compromised or hallucinating multi-agent chain from dropping tables, draining API quotas, or exfiltrating personally identifiable information (PII) via Model Context Protocol (MCP) servers.

Most enterprises attempt to control models, but they do not control execution paths.

To scale agentic AI safely across regulated industries, financial institutions, and complex distributed networks, you must build a continuous AI agent discovery pipeline combined with an inline, deterministic runtime enforcement layer.

Autonomous System Architecture & Challenges

Modern agentic systems diverge fundamentally from deterministic microservices and classical MLOps pipelines. A standard service implements rigid, compile-time control logic. An autonomous agent, conversely, treats control flow as an emergent runtime property determined by non-deterministic model inference.

Agents, Tools, and Orchestrators

At the foundational tier sits the orchestrator (LangGraph, AutoGen, CrewAI, or bespoke internal loops). The orchestrator maintains conversational context, system prompts, memory stores (vector indexes, Redis caches), and an array of tool definitions. These tools are formatted as JSON schema declarations passed to model APIs via function calling interfaces.

Multi-Agent Workflows and Chaining

Agents rarely operate in isolation. Advanced architectures leverage hierarchical topologies: a "Supervisor" or "Planner" agent ingests a complex business goal (e.g., "Reconcile cross-border transaction discrepancies for Q3"), decomposes it into discrete programmatic actions, and delegates those sub-tasks to specialized domain agents (e.g., an SQL Query Agent, an Accounting System Connector, and an Email Dispatcher).

This sub-task delegation introduces asynchronous execution chaining. Execution depth cascades unpredictably. Agent A spawns Agent B, which evaluates a condition and recursively invokes Agent C with dynamic parameters that no human developer explicitly hardcoded.

Tool Invocation Mechanics (APIs, Databases, SaaS)

Tool execution transforms deterministic infrastructure into the target of non-deterministic model outputs. Tools are invoked through:

• Direct REST/gRPC Calls: Agents construct dynamic payloads to query services.
• Database Connectors: Text-to-SQL agents running raw queries against operational data lakes or transactional databases.
• Model Context Protocol (MCP): The emerging open standard initiated by Anthropic that standardizes how local or remote clients expose prompts, tools, and context to AI agents. MCP servers expose local endpoints that directly execute shell commands, read filesystem paths, or query internal tools.

Identity Propagation Gaps

In standard microservice architectures, user context propagates via down-scoped tokens containing claims (sub, roles, scope) down the call stack.

In agentic chains, this chain of custody often shatters at the orchestrator boundary.

Agents frequently execute with the broad, ambient permissions of an underlying service account or an administrative API key. If User X prompts an agent to read a document, the agent often queries the document store not as User X, but as agent-runner-prod-sa.

The downstream system loses end-user context. It cannot determine whether the original caller had legitimate read rights, rendering downstream Role-Based Access Control (RBAC) ineffective.

Token Issuance and Validation (JWT/JWKS)

Where identity is implemented, it often lacks dynamic delegation semantics. Standard JSON Web Tokens (RFC 7519) validated against JSON Web Key Sets (JWKS) confirm the authenticity of a caller, but they do not confirm the intent of an intermediate agent step.

Without RFC 8693 OAuth 2.0 Token Exchange patterns natively integrated into agent tool-use lifecycles, down-scoping tokens dynamically for single, ephemeral tool invocations remains exceedingly rare in custom enterprise deployments.

Runtime Execution vs. Orchestration Logic

Orchestration frameworks manage state machines, retry policies, and prompt templates. They do not provide isolated, low-latency, deterministic network security. Confusing an agent framework with a security boundary is an architectural anti-pattern.

If security controls reside purely inside prompt engineering or inside framework-level Python callbacks (before_tool_call), a prompt injection, memory poisoning, or Python runtime exploit can bypass those checks completely.

Centralized enforcement requires separating orchestration logic from the execution path. Security enforcement must sit squarely in the network transport layer.

Autonomous System Risks

When non-deterministic models execute side effects within deterministic, high-value corporate environments, security risks manifest at the execution layer.

Action Risk

Action risk encompasses unauthorized, unintended, or maliciously coerced state mutations across enterprise endpoints.

• Unauthorized API Execution: An agent modifies critical records (e.g., setting enterprise customer discount rates to 100%) because its LLM misjudged the semantic boundary of a prompt.
• Tool Misuse & Semantic Drift: An agent supplied with a wide-scoped tool (such as execute_bash or run_custom_query) constructs unintended system calls to fulfill a vaguely structured user instruction.
• Privilege Escalation: By passing untrusted inputs into chained tools, an attacker forces an agent to call administrative functions exposed by internal APIs that lack parameter-level authorization.

Data Risk

• Sensitive Data Leakage: Agents processing semi-structured corporate data inadvertently pull protected health information (PHI), PII, or internal credentials into external API payloads or outbound model context windows.
• RAG Over-Exposure: Retrieval-Augmented Generation engines often index vast amounts of internal data without mapping vector embeddings back to granular, user-level Access Control Lists (ACLs). An unprivileged user querying an agent can extract sensitive HR compensation files or pre-release earnings data because the RAG ingestion pipeline lacked authorization parity with the source system.
• Cross-Tenant Contamination: In multi-tenant environments, poorly isolated agent memory buffers, shared vector stores, or cached tool connections permit one tenant's agent chain to inspect or mutate another tenant's data state.

Financial Risk

• Runaway Execution Costs: Recursive agent workflows running unbounded planning loops can make thousands of model invocations per minute, rapidly burning operational budgets.
• Uncontrolled Financial Transactions: Autonomous procurement or customer refund agents operating without hard, deterministic monetary caps execute fraudulent or duplicate settlements.
• API Abuse & Third-Party Rate Limit Exhaustion: Flooding downstream SaaS platforms with unthrottled requests triggers upstream rate-limiting, cascading service degradation across the business.

Operational Risk

• Cascading Failures: When an upstream tool fails, an agent might branch into recursive retry cycles or attempt nonsensical fallback paths, creating a storm of redundant calls that can take down internal infrastructure.
• State Machine Corruption: Non-deterministic tool parameters can introduce schema violations into mission-critical ERPs and CRMs, corrupting transactional consistency.

Compliance & Regulatory Risk

• Audit Gaps: Many agent tools execute via ephemeral, direct-from-code network connections. When regulatory inquiries demand a record of why a customer record was deleted, the organization cannot present a non-repudiable audit trail linking the initial user prompt to the exact model decision, policy check, and tool call payload.
• Regulatory Non-Compliance: Unchecked agent actions violate standards such as the NIST AI Risk Management Framework (AI RMF 1.0), SEC disclosure mandates, HIPAA, and GDPR Article 22 regarding automated individual decision-making.

Table 1: Autonomous System Risks vs. Business Impact

Interoperability & Vendor Lock-In Risks

As organizations rush to integrate autonomous workflows, architectural fragmentation across internal stacks introduces severe operational risks.

The Traps of Early Agent Adoption

1. Proprietary Connectors and Vendor SDKs: Early agent development frequently relies on proprietary connectors provided by cloud hyperscalers or framework vendors. These connectors embed tool execution within closed abstractions, preventing cross-cloud orchestration and hiding the underlying network mechanics from security teams.
2. Embedded Policy Logic in Orchestration Frameworks: Encoding authorization logic into prompt instructions or framework-specific Python wrappers tightly couples governance policies to specific models. If an enterprise migrates from a commercial model API to an open-weights model, prompt-based guardrails often fail to generalize, requiring teams to rebuild their governance policies from scratch.
3. Non-Standard Token Formats: Inconsistent identity handling across disparate agent runtimes results in brittle, custom translation layers. When Agent Framework X cannot interpret the identity claims issued by Agent Framework Y, teams often drop down to wide-open, static API keys to maintain velocity, creating massive security gaps.
4. Closed Telemetry Systems: Proprietary platforms often output monitoring metrics to siloed, vendor-managed dashboards. These proprietary logs strip essential context—such as model temperature, system prompts, specific tool schemas, and downstream network latency—leaving security operations centers (SOCs) unable to correlate agent anomalies with traditional SIEM/SOAR signals.

Modern Architectural Solutions

To maintain portability, security, and enterprise control, platform architects must decouple the agent development layer from the security, identity, and governance tier:

• Protocol Standardization (HTTP/gRPC, JWT): Decouple agent communication using open standards. All agent-to-tool and agent-to-agent interactions must resolve across standard HTTP/2, gRPC, or MCP interfaces, wrapping identities in standards-compliant OAuth2 JWTs.
• Adapter-Based Integration Architecture: Isolate framework-specific code behind strict interfaces. If a business unit constructs an agent using an emerging library, that agent must route calls through standard egress adapters rather than communicating with internal infrastructure directly.
• Externalized Policy Bundles (Open Policy Agent / OPA): Abstract security policies away from application code into declarative, mathematically verifiable rules written in Rego. These policy bundles are versioned in Git, tested in continuous integration (CI) pipelines, and distributed out to enforcement points across your infrastructure.
• Neutral Telemetry (OpenTelemetry): Standardize all semantic traces, metrics, and logs on the OpenTelemetry standard. Emitting normalized generative AI semantic conventions ensures that your security and observability pipelines can analyze trace events regardless of which agent framework generated them.

Runtime Control Architecture

Static discovery without real-time interception cannot protect enterprise data. If an unmanaged agent spins up, attempts an unauthorized data extract, or triggers an unexpected transaction, an enterprise needs an infrastructure-level mechanism to evaluate, intercept, and block the execution payload before it reaches the target system.

This requires an inline Runtime Control Architecture.

Core Architectural Components

Gateway / Reverse Proxy (The Envoy Pattern)

At the heart of the runtime enforcement layer sits a high-performance proxy modeled on the Envoy Proxy architecture. The proxy intercepts all outbound tool invocations, API calls, and MCP server requests originating from agent compute nodes. Because it operates at Layers 4 and 7, agents cannot bypass the proxy through application-level misconfigurations or prompt injection exploits.

External Authorization (ext_authz)

Envoy’s ext_authz filter pauses incoming HTTP requests or gRPC calls, serializes the call context (HTTP method, target path, headers, client certificate metadata, and JSON request body), and dispatches a check request to an external authorization service before routing the traffic onward.

Policy Engine (OPA Bundles)

The authorization service processes the request payload using an embedded Open Policy Agent engine. The engine runs compiled Rego policies against the structured payload, evaluating factors such as:

• The cryptographic identity and organizational group of the agent.
• The specific tool, endpoint, and action requested.
• The parsed JSON parameters (e.g., verifying that transaction_amount is under a set threshold).
• Contextual environmental data (e.g., source IP, time of day, current system risk posture).

The engine returns an unambiguous decision: ALLOW, DENY, or REQUIRE_HUMAN_APPROVAL.

Dynamic Token Exchange Engine

If the policy engine grants access, the runtime layer uses an RFC 8693 token exchange service. It exchanges the agent’s ambient token for a short-lived, cryptographically restricted, and down-scoped credential dedicated exclusively to the target tool. This ensures the target system receives an identity asserting least-privilege access for that single interaction.

Unified Observability Pipeline (OpenTelemetry)

Every transaction—permitted, rejected, or modified—is captured by an inline OpenTelemetry collector. The system records the complete call lifecycle: prompt hashes, agent identities, policy evaluation outputs, tool execution latencies, and response payloads. Sensitive values are redacted before these events leave the proxy perimeter.

Enterprises can implement this architecture by leveraging specialized platforms like Aegis Security. Aegis provides a purpose-built runtime control plane designed to intercept, authorize, and audit autonomous agent actions at wire speed without introducing friction into the developer experience.

 Governance & Control Model

Scaling agentic systems across heavily regulated environments requires moving beyond manual checklists to a continuous, machine-enforced governance framework.

A modern governance program must maintain five key pillars:

Cryptographic Identity (Who is acting?)

Every autonomous agent must have a distinct, verifiable machine identity. Relying on shared service accounts makes attribution impossible.

• Implement workload identity frameworks such as SPIFFE/SPIRE or dedicated JWT-based agent identity models.
• Cryptographically bind the human initiator, the specific agent execution run, and the orchestrator version to every outbound network payload.

Externalized Policy (What is allowed?)

Authorization policies must remain independent of agent prompts, application code, and model vendors.

• Write deterministic, unit-tested policies as code using OPA (Rego) or Cedar.
• Decouple governance configurations so that security teams can update rules globally—such as blocking access to a newly discovered vulnerable tool—without touching application code or redeploying agent containers.

Comprehensive Observability (What happened?)

• Capture full, structured runtime execution traces across every agent transition.
• Capture every parameter passed to tool interfaces, tool execution responses, and the corresponding decision logic.
• Standardize on OpenTelemetry semantic conventions for generative AI and agentic workflows to ensure telemetry easily ingests into your existing enterprise monitoring stack.

Non-Repudiable Auditability (Can it be proven?)

• Store security-relevant execution events in tamper-evident, append-only storage systems.
• Ensure your audit logs can definitively prove the causal chain of events: which user prompt triggered the agent, what inference led to the tool call, which policy evaluated the call, and what response was returned by downstream infrastructure.

Deterministic Human-in-the-Loop (When is approval required?)

Human oversight should be reserved for high-consequence operations. Relying on humans to review every mundane action causes alert fatigue and negates the efficiency gains of automation.

• Define clear threshold policies that programmatically route only high-risk actions—such as transactions exceeding $10,000, database schema updates, or outbound mass communications—to human-in-the-loop (HITL) authorization queues.
• Expose asynchronous approval hooks via secure messaging endpoints (e.g., Slack APIs, PagerDuty, Jira Service Desk) using short-lived authorization tokens.

Table 2: Control Mechanisms vs. Risk Mitigation

Enterprise Failure Scenarios

To understand the urgent need for runtime enforcement, we must analyze how unmonitored agent systems fail in real-world enterprise environments. The root risk of agentic AI is not models becoming "too smart"; the real risk is uncontrolled actions executing inside trusted systems.

Incident 1: The Automated Financial Runaway

• The Failure: A global fintech organization deployed an autonomous customer support agent authorized to issue account concessions and dispute refunds up to $250. An edge-case dispute loop, compounded by an ambiguous system prompt and an intermittent network error, caused the agent to view repeated timeouts as failed delivery attempts. The agent entered an unmonitored retry loop, issuing 437 consecutive maximum-allowable refunds to a single customer cohort over a weekend.
• Why it Happened: The orchestrator lacked deterministic transactional rate limits. Security teams relied on prompt-level instructions ("Do not issue more than one refund per user") rather than enforcing hard transactional state limits at the network layer.
• The Lesson: Business logic written in English prompts is non-deterministic. Hard financial caps must be enforced by an external, deterministic policy engine operating independently of model inference.

Incident 2: Cross-Tenant Data Exfiltration via RAG

• The Failure: A healthcare technology provider built an internal research agent to summarize clinical operational reports. The agent was integrated with enterprise RAG infrastructure indexed across multiple clinic networks. An unprivileged user submitted an adversarial prompt containing an indirect injection instruction embedded within a public insurance PDF. The agent processed the document, bypassed the user's role limits via its ambient service account, and appended another clinic’s unredacted patient records directly into the public response chat.
• Why it Happened: The RAG retrieval pipeline lacked document-level authorization parity. The agent’s query interface used an administrative credential that could read all underlying vector indexes, relying on the model to filter out information the user was not authorized to see.
• The Lesson: AI agents must never query underlying data platforms with ambient administrative privileges. Access tokens must be dynamically down-scoped to match the querying user's exact rights before data retrieval runs.

Incident 3: Infrastructure Corruption via MCP Tool Misuse

• The Failure: A software enterprise integrated internal development agents with local developer machines and cloud staging servers using an experimental Model Context Protocol (MCP) server. An engineer instructed an agent to "clean up lingering test containers and update the staging service definitions." The agent parsed the ambiguous command, generated a destructive shell command (rm -rf across a mounted volume containing the primary staging database data directory), and executed it via the local MCP shell tool.
• Why it Happened: The MCP server ran with the ambient permissions of the root Docker daemon. There was no inline proxy intercepting the tool payload to evaluate the command against a prohibited system actions list.
• The Lesson: Tool invocation requires deep payload inspection. High-risk commands must be matched against strict, deterministic rule sets and blocked before execution, regardless of the agent's confidence score.

Business & Operational Value: Enabling Scale Through Control

A common misconception among platform engineers is that embedding runtime security slows down innovation. In production, the opposite is true: uncontrolled systems cannot scale.

According to industry data from organizations like Gartner and McKinsey, over 40% of agentic AI initiatives stall out in proof-of-concept stages or face post-deployment shutdowns due to governance gaps, unquantified execution risks, and compliance hurdles.

Implementing an autonomous agent discovery and runtime security layer provides distinct business dividends:

Radically Reduced Blast Radius

By enforcing least-privilege access at the network boundary, you ensure that even if an agent's reasoning loop is compromised, its operational reach is strictly capped. An agent with access to an internal payment system cannot be coerced into dropping a database table if the runtime proxy blocks unauthorized database paths.

Painless Compliance and Audit Readiness

Adopting OpenTelemetry semantic conventions and externalized OPA policies transforms audit preparation from a multi-month forensic scramble into an automated reporting process. When regulators operating under frameworks like the EU AI Act or NIST AI RMF request records of automated decision paths, you can provide an immutable log proving the model inputs, identity contexts, and policy checks behind every action.

Confident, Accelerated Deployment of Autonomous Systems

When platform teams know that an inline runtime engine will catch aberrant actions, financial loops, and injection attempts, they can safely move from read-only assistants to high-value, state-mutating agents.

Long-Term Vendor Flexibility

Externalizing policies into open formats like OPA and standardizing telemetry on OpenTelemetry eliminates vendor lock-in. Your enterprise can swap out models, orchestrators, and cloud platforms as the competitive landscape shifts, confident that your governance layer remains intact.

To see how modern enterprises discover shadow agents, catalog execution surfaces, and enforce runtime security policies without rewriting application code, explore the platform at Aegis Security or Book a Demo with our technical architecture team.

Technical Architecture Deep-Dive & Glossary

To build effective agent discovery and runtime enforcement, systems engineers must understand these foundational components:

Policy-as-Code

The practice of managing security, authorization, and compliance rules as version-controlled, declarative code. Rather than writing authorization rules inside prompt text or hardcoding them into application code, policies are written in domain-specific languages (such as Rego for OPA). These policies are checked into Git, covered by automated test suites, and pushed dynamically to runtime proxies without requiring application restarts.

ext_authz (External Authorization)

A standard network filter protocol popular in cloud-native proxies like Envoy. When a connection attempts to route through the proxy, the ext_authz filter pauses the request and dispatches a lightweight gRPC or HTTP check request containing the call context to an external authorization service. The proxy holds the request in memory until the authorization service returns a decision.

OPA Bundles

A mechanism used by the Open Policy Agent to distribute compiled policy sets, configuration metadata, and operational data. Bundles are packaged as compressed .tar.gz files, cryptographically signed, and served via standard HTTP servers. OPA instances running alongside proxies continuously poll for bundle updates, dynamically activating new policies within milliseconds.

JWT & JWKS

• JSON Web Token (JWT): A compact, URL-safe means of representing claims to be transferred between two parties (RFC 7519). In agent architectures, JWTs carry cryptographically signed identity claims verifying which user initiated an agent run and which agent instance is executing.
• JSON Web Key Set (JWKS): A JSON object that contains a set of public keys used by resource servers to verify cryptographic signatures on incoming JWTs (RFC 7517), enabling dynamic key rotation.

Token Exchange (RFC 8693)

An OAuth 2.0 extension defining how an entity can exchange an existing security token for a different token with adjusted parameters. In an agent workflow, an orchestrator presents an incoming user token to a security token service (STS) and exchanges it for a down-scoped, short-lived token valid only for calling a specific tool endpoint. This prevents compromised agents from reusing tokens across multiple systems.

OpenTelemetry (OTel)

A vendor-neutral, open-source observability framework under the Cloud Native Computing Foundation (CNCF). It provides a unified collection of APIs, SDKs, and tooling to generate, collect, and export telemetry data (traces, metrics, and logs). Standardizing agent semantic conventions within OpenTelemetry allows teams to correlate LLM latency, system prompts, token usage, and tool invocations within a single operational trace.

Agent Identity (SPIFFE / Workload Identity)

The cryptographic attribution assigned to an autonomous software agent. Distinct from the human user who prompted it, an agent identity allows downstream systems to independently verify the running binary, the hosting node, and the service boundaries of the agent before granting access to internal resources.

Runtime Enforcement

The deterministic interception and validation of requests while they are in transit across the network data plane. Unlike static security scans run during CI/CD or prompt evaluations run inside application logic, runtime enforcement operates at the transport layer, inspecting and modifying network traffic before it reaches target infrastructure.

Shadow Mode (Dry-Run Evaluation)

A deployment pattern where a newly introduced policy runs inline against live production traffic, evaluating real-world agent calls and logging whether it would have permitted or blocked each request—without actually dropping traffic. Shadow mode allows security teams to baseline agent behavior, catch policy bugs, and measure business impact before enforcing blocking rules.

Comprehensive Guide: AI Agent Discovery Across the Enterprise

You cannot govern what you cannot see. Establishing an inventory of what is running is the mandatory first step toward enterprise runtime enforcement.

The proliferation of shadow agents across modern enterprises requires a continuous discovery methodology combining four distinct detection vectors:

Telemetry-Based Discovery (OpenTelemetry Scanners)

As development teams adopt frameworks like LangChain, LlamaIndex, and AutoGen, these libraries increasingly emit OpenTelemetry spans by default.

• Implementation: Deploy stream processors and collectors that continuously inspect your centralized OTel trace ingestion pipeline. Configure listeners to look for agent framework signatures, specific span names (such as ai.tool_call, ai.agent.run, or llm.generate), and common prompt evaluation tags.
• Coverage: This captures actively instrumented production workloads across application development teams, providing immediate insights into which models and tools are actively in use.

Model Context Protocol (MCP) Server Monitoring

The Model Context Protocol has emerged as the standard mechanism for connecting agent reasoning engines to underlying compute systems.

• Implementation: Actively scan enterprise compute clusters, developer endpoints, and internal networks for active MCP endpoints. Monitor both stdio and HTTP/SSE-based MCP transports.
• Coverage: This detects internal tools exposed to AI assistants, identifying undocumented local tools, unauthenticated developer servers, and shadow bridges into corporate file shares or databases.

Network-Layer Egress Analysis

Even if an agent runs without OpenTelemetry instrumentation or relies on a custom orchestration script, it must communicate with an inference engine to reason.

• Implementation: Deploy Layer 7 network inspection proxies across your enterprise egress routes. Inspect TLS traffic headers and payload signatures to detect outbound traffic directed toward commercial model endpoints (OpenAI, Anthropic, Mistral) and internal model servers (vLLM, Ollama, Triton).
• Coverage: Network inspection acts as your safety net, catching shadow agents that bypass application-level logging or run in unmanaged environments.

API-Driven Cloud Infrastructure Discovery

Hyperscale cloud providers now expose dedicated control planes for agent services (e.g., AWS Bedrock Agents, Google Vertex AI Agent Builder, Azure AI Foundry).

• Implementation: Configure automated scanning services that authenticate with your enterprise cloud environments via read-only administrative roles. Query cloud provider APIs continuously to enumerate running agents, inspect registered tool definitions, and identify provisioned service accounts.
• Coverage: This discovers top-down, cloud-managed agents configured directly within cloud consoles by business teams, identifying resources that may have bypassed standard GitOps deployment pipelines.

Step-by-Step Implementation: Building Your Agent Inventory

To transition your organization from fragmented visibility to active runtime enforcement, implement this phased deployment roadmap:

From Visibility to Active Control

Building a comprehensive agent inventory is a critical milestone, but an inventory alone will not prevent an unauthorized database update or stop a prompt-injection attack in progress. Static tracking must serve as the foundation for active runtime governance.

By coupling continuous multi-vector discovery with an inline proxy layer that enforces policy as code, security teams can give their organizations the freedom to build and deploy autonomous agents at scale—with the assurance that every execution path is monitored, authorized, and completely under enterprise control.

Enterprise Next Steps

1. Conduct an Internal Surface Audit: Run an immediate egress analysis across your network boundaries to identify undocumented calls to commercial LLM APIs.
2. Standardize on OpenTelemetry: Establish OTel as your organization's mandatory baseline for all software services that interact with machine learning models and external tools.
3. Decouple Security Policies from Prompts: Begin migrating authorization logic out of application code and system prompts into externalized, testable OPA policy bundles.
4. Deploy Inline Runtime Protection: Evaluate platforms built for real-time agent interception. Explore how Aegis Security can integrate with your existing cloud infrastructure to provide continuous discovery, runtime control, and zero-trust tool governance. Book a technical architecture demo to see Aegis in action.