8 AIDR Platforms for Runtime Agent Monitoring

An AI agent can look perfectly well-behaved in a demo and very different on a Tuesday afternoon in production. It reads a document that contains hidden instructions, calls a tool it rarely uses, reaches into a file it has never touched, and starts sending data somewhere new. Each step uses permissions the agent legitimately holds. Nothing crashes, no malware runs, and most traditional security tools see nothing unusual.

Runtime agent monitoring exists to see those moments as they happen. AIDR platforms watch what agents actually do once they are running, detect when that behavior turns risky, and respond before the damage is done. The eight platforms below take different routes to that goal, and the right choice depends heavily on where an organization’s agents run and what they can reach.

What Runtime Agent Monitoring Has to Capture

Monitoring an agent is not the same as logging its outputs. To catch risky behavior in time, a platform needs visibility into several layers of every session.

1. Where instructions come from

Agents take direction from users, system prompts, retrieved documents, tool descriptions, and other agents. Monitoring must distinguish between what a user asked for and what an agent picked up from content along the way, because indirect instructions are a common path for manipulation.

2. What the agent plans to do

Many agents reason through a plan before acting. Seeing that plan, or inferring it from the sequence of actions, helps detect when an agent is heading toward a harmful outcome before it gets there.

3. Which tools it calls and with what arguments

Tool calls are where intent turns into action: shell commands, file reads and writes, API requests, MCP server calls, and database queries. The arguments matter as much as the tool, since the same command can be safe or dangerous depending on its target.

4. What data moves where

Sensitive data can flow into prompts, out through tool calls, or into responses and logs. Monitoring data movement across the session reveals leakage that a single event would not.

5. Whose authority the agent is using

Agents act with user credentials, service identities, and tokens. Knowing which identity sits behind each action, and whether that identity should be doing it, is essential for detecting privilege misuse.

6. How the session evolves over time

The most important signal is often the trajectory. A session that starts as a code fix and ends with credential collection may contain no individually suspicious step. Tracking intent across the whole session is what exposes that drift.

The 8 AIDR Platforms for Runtime Agent Monitoring

1. Dash Security

Runtime monitoring is only as good as the context behind it. Dash Security, the security and control plane for AI agents, builds that context before it ever evaluates an action. It discovers known and shadow agents across workstations and managed cloud platforms, along with the MCP servers, skills, plugins, models, and connected systems they rely on, and maps them into an Agentic FootPrint of the organization’s full agentic estate.

From there, Dash profiles every agent, user, and session. It builds an understanding of each agent’s purpose and capabilities, each user’s intent and behavior history, and the event trajectory of every AI session, enriched with signals from identity providers and endpoint tools. Its proprietary native integration collects signals from multiple stages of each agentic turn, creating a dense layer of runtime context that reveals anomalies leaner telemetry misses.

Detection is built on intent. Dash uses intent similarity to evaluate what an agent is trying to do and identifies intent drift when a session moves away from its original purpose, which helps separate harmful behavior from legitimate activity that looks similar on the surface. It detects data leakage, unsafe commands, and the risks defined in the OWASP Top 10 for Agentic Applications, while AI DLP monitors sessions for sensitive data exposure. Response is proportionate to risk: Dash can inform, prevent, remediate, or export events, require human approval inside a live session, stop agents from bypassing permissions and instructions, and harden guardrails in runtime.

Coverage is broad and deployment is fast. Dash provides runtime protection for more than 20 coding agents in CLIs and IDEs, along with desktop and browser assistants and autonomous agents across networks and cloud, and discovers more than 60 unique AI platforms. Its modular, single-sensor, agentless deployment runs across Linux, macOS, and Windows, and organizations can move from discovery to enforcement within a week. Complete session traceability supports investigation, blast-radius assessment and root-cause analysis translate into action inside the platform, and native integrations plus the Dash MCP server connect it to existing security workflows without vendor lock-in. Dash is backed by YL Ventures, Wing, and Vesey Ventures, and its founders held leadership roles at Palo Alto Networks, Akamai, and IBM.

Key features:

• Agentic FootPrint discovery of agents, MCP servers, skills, plugins, and shadow AI
• Agent, user, and session profiling with identity and endpoint context
• Signal collection across multiple stages of each agentic turn
• Intent similarity and intent drift detection
• Detection of data leakage, unsafe commands, and OWASP agentic risks
• Inform, prevent, remediate, and export actions, plus in-session human approval
• Runtime protection for 20+ coding agents and discovery across 60+ platforms
• Agentless deployment with discovery to enforcement in a week

2. Proofpoint AI Security

Proofpoint entered agent security through its February 2026 acquisition of Acuvity, which delivered runtime inspection and enforcement across applications, agents, and MCP servers. Proofpoint AI Security, launched in March 2026, applies intent-based detection to evaluate whether AI behavior aligns with the original request, defined policies, and intended purpose, across endpoints, browsers, and MCP agent connections.

Proofpoint pairs its AI security capabilities with its established data security and collaboration security portfolio and has introduced an Agent Integrity Framework with a five-phase maturity model, from discovery through runtime enforcement. For organizations already invested in Proofpoint, the approach extends familiar data and human-risk controls to AI agents.

Key features:

• Runtime inspection across endpoints, browsers, and MCP connections
• Intent-based detection of AI behavior
• Agent Integrity Framework and maturity model
• Integration with Proofpoint data and collaboration security

3. Oligo Security

Oligo approaches AIDR from application runtime security. Its AI-DR module monitors AI agent behavior in production, including tool usage, API calls, and code execution, while its AI-SPM module maintains a live inventory of models, agents, SDKs, and external AI services running in applications.

Because Oligo observes behavior at the application and library level, it is well suited to organizations building their own agents and AI applications that run in cloud workloads. It also integrates with AWS Security Hub.

Key features:

• Runtime monitoring of agent tool use, API calls, and code execution
• Live inventory of models, agents, and AI services
• Application-level runtime protection
• AWS Security Hub integration

4. Knostic

Knostic focuses on guardrails for AI agents, applying least privilege to what agents do. Its Kirin offering checks every agent action against policy before it runs, allows approved actions, and blocks risky ones or sends them to a human for review, with the same policy applied across agents.

Knostic emphasizes that instructing a model to behave is not enough, so enforcement happens at the action level. Decision logs and runtime monitoring provide evidence for audits and incident response, which is useful for teams that need clear policy enforcement around coding and endpoint agents.

Key features:

• Least-privilege enforcement for agent actions
• Monitoring of every tool call
• Pre-execution blocking or human escalation
• Decision logs for audit and incident response

5. Aurascape

Aurascape provides inline visibility and control over AI interactions, covering both the AI tools employees use and the applications and agents teams build. For agents, it offers discovery, pre-deployment adversarial testing, and runtime governance of outputs and MCP tool calls.

Its single-platform approach appeals to organizations that want one team to govern employee AI use and homegrown agents together, rather than managing separate products for each.

Key features:

• Inline visibility into AI interactions
• Governance for employee AI and homegrown agents
• MCP tool-call governance
• Pre-deployment adversarial testing

6. Varonis Atlas

Varonis brings a data security perspective to AI and agents through Atlas, which connects AI security posture management with the data agents can access. Understanding which sensitive data an agent can reach helps prioritize monitoring and limit exposure.

Atlas includes runtime guardrails, and its strength lies in tying agent activity to the sensitivity and permissions of underlying data, a natural fit for organizations already using Varonis for data security.

Key features:

• AI security posture tied to data exposure
• Visibility into data accessible by agents
• Runtime guardrails
• Integration with Varonis data security

7. Galileo

Galileo comes from AI reliability and observability. Its platform helps teams evaluate and monitor agents in production, tracing multi-step workflows and tool use, and its real-time protection capabilities can intercept problematic inputs and outputs.

Galileo is used primarily by AI engineering teams that need to understand agent quality and failures, and its observability complements security-focused monitoring for organizations building their own agents.

Key features:

• Production observability for agent workflows
• Tracing of multi-step tool use
• Evaluation of agent quality and failures
• Real-time input and output protection

8. Harmonic Security

Harmonic Security focuses on the data employees and AI tools exchange, detecting sensitive information as it flows into AI applications and enforcing policies to prevent exposure. Its models are designed to classify sensitive content in real time with low friction for users.

For organizations whose main runtime concern is data leakage through AI usage, Harmonic adds a focused layer of protection that complements broader agent monitoring.

Key features:

• Real-time detection of sensitive data in AI interactions
• Policy enforcement for AI data sharing
• Low-friction user experience
• Coverage of employee AI tools

How to Choose by Agent Footprint

Engineering organizations with heavy coding agent use

When developers run coding agents in IDEs and terminals with access to repositories, credentials, and shells, monitoring must sit close to the workstation and understand session intent. Platforms with deep coverage of coding agents and the ability to require approval mid-session protect productivity without leaving risky commands unchecked.

Enterprises adopting low-code and SaaS agent platforms

Agents built in enterprise platforms often connect to CRM, ticketing, and document systems. Look for discovery across those platforms, governance of what agents can access, and runtime controls on the actions they take inside business systems.

Teams building homegrown agents in the cloud

Agents running as cloud services benefit from application-level runtime monitoring and observability that trace tool use, API calls, and code execution in production, combined with policy enforcement for risky actions.

Data-sensitive and regulated organizations

Where data exposure is the primary concern, prioritize platforms that connect agent behavior to data sensitivity, detect leakage in real time, and provide complete session records for audit and incident response.

Most enterprises fit more than one of these profiles, which is why coverage across agent types and environments from a single control plane carries significant weight.

FAQ

What is runtime agent monitoring?

Runtime agent monitoring is the continuous observation of AI agents while they operate, including the instructions they receive, the tools they call, the data they access, and how their sessions evolve. It allows security teams to detect and stop risky behavior in real time rather than discovering it after an incident.

How does AIDR relate to runtime agent monitoring?

AIDR, or AI detection and response, uses runtime monitoring as its foundation. Monitoring provides visibility into agent behavior, while AIDR adds detection logic to identify threats and response capabilities to block, constrain, or escalate risky actions.

Why is logging prompts and responses not enough?

Many risky actions happen in tool calls, file access, and data movement rather than in visible responses. An agent can produce a harmless answer while quietly reading credentials or sending data elsewhere, so monitoring must cover actions across the whole session.

What is intent drift?

Intent drift occurs when an agent’s behavior moves away from the purpose of the original request, often because of manipulated instructions. Detecting it requires comparing actions with what the agent was asked to do, as Dash Security does with intent similarity and intent drift detection.

Can runtime monitoring slow agents down?

Well-designed platforms add minimal latency and apply controls only where risk warrants them. Proportionate responses, such as allowing most actions while requiring approval for high-risk ones, keep agents productive while maintaining security.

Which agents should be monitored first?

Start with agents that have broad permissions or access to sensitive data, such as coding agents with shell and repository access, and agents connected to business-critical systems. Discovery helps identify these quickly, including shadow agents installed without approval.