Last Updated: 2026-06-28

As AI agents become integral to modern software, securing their operation is paramount. This guide is for developers building and deploying agentic workflows who need to leverage observability tools for robust security. We'll provide a direct technical overview of leading AI agent observability tools that help detect anomalies, prevent data exfiltration, and secure your agent's entire lifecycle in 2026.

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The Imperative of AI Agent Security Observability

Agentic AI systems introduce novel security challenges that traditional application security models often miss. Beyond standard software vulnerabilities, developers must contend with prompt injection, data exfiltration through tool use, non-deterministic behavior, and supply chain risks from external APIs and models. Observability, in this context, extends beyond performance monitoring to include comprehensive logging, tracing, and metric collection specifically tailored to agent interactions, decisions, and tool usage. This allows for real-time detection of anomalous behavior that could indicate a security breach, misuse, or unintended consequences.

Securing agentic workflows requires a multi-faceted approach:
* Pre-runtime Security: Scanning the agent's code, dependencies, and infrastructure for vulnerabilities.
* Runtime Observability: Monitoring agent execution, tool calls, data access, and LLM interactions for suspicious patterns.
* Post-incident Analysis: Using collected telemetry to understand the root cause of security events.

This article focuses on tools that enable this holistic security posture, emphasizing how traditional observability and security scanning tools adapt to the unique demands of AI agents. For a broader look at general AI agent observability, refer to our guide on 15 Best AI Agent Observability Tools in 2026 (AgentOps & Langfuse).

Comparison Table: AI Agent Observability & Security Tools

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Deep Dive: Best AI Agent Observability & Security Tools

Datadog

Datadog offers a comprehensive observability platform that has rapidly evolved to support AI agent workflows, particularly with its LLM Observability add-on. This makes it a strong contender for securing agentic applications by providing deep visibility into LLM interactions, tool usage, and overall agent behavior.

• Best for:

  • Full-stack observability of complex agentic applications, from infrastructure to LLM calls.
  • Detecting prompt injection attempts and data exfiltration through LLM interactions.
  • Unified monitoring of agent performance and security metrics.

• Pros:

  • Dedicated LLM Observability features provide specific insights into agent prompts, responses, and token usage.
  • Watchdog AI anomaly detection can flag unusual agent behavior indicative of security threats.
  • Integrates seamlessly across logs, metrics, traces, and RUM for a complete picture.

• Cons:

  • Can become expensive at scale due to usage-based pricing.
  • Initial setup for custom agent telemetry might require significant instrumentation.

• Pricing: Datadog offers a free trial, with paid plans based on usage across various products (e.g., infrastructure, logs, APM, LLM Observability).

New Relic

New Relic provides a powerful full-stack observability platform with a strong emphasis on AIOps, making it highly effective for identifying security anomalies in agentic workflows. Its free tier is generous, allowing developers to get started without immediate cost.

• Best for:

  • Developers needing a unified platform for monitoring agent performance and security.
  • Leveraging AIOps to automatically detect and alert on unusual agent behavior or potential security incidents.
  • Teams looking for a cost-effective entry point with a substantial free tier.

• Pros:

  • Applied Intelligence for AIOps automatically correlates events and detects anomalies, reducing alert fatigue.
  • Offers comprehensive tracing for agent interactions, including external API calls and tool usage.
  • Generous free tier (100GB/month ingest) is suitable for many development and small-scale deployments.

• Cons:

  • While strong in AIOps, specific LLM-centric security features might require custom instrumentation.
  • Steep learning curve for new users due to the breadth of features.

• Pricing: New Relic provides a free tier with 100GB of data ingest per month. Beyond these limits, paid tiers are available based on data ingest and user count.

Dynatrace

Dynatrace stands out with its Davis AI engine, which provides automated root-cause analysis across complex environments. For AI agents, this means quicker identification of security-related issues, from infrastructure vulnerabilities to anomalous agent behavior.

• Best for:

  • Automated root-cause analysis of security incidents within complex agentic systems.
  • Organizations requiring full-stack auto-instrumentation for rapid deployment and visibility.
  • Proactive detection of security threats through AI-driven anomaly detection.

• Pros:

  • Davis AI engine automatically identifies the root cause of performance and security issues, including those related to agent misbehavior.
  • Full-stack auto-instrumentation simplifies deployment and ensures comprehensive data collection.
  • Strong capabilities for monitoring microservices and cloud-native environments where agents often reside.

• Cons:

  • Can be a premium solution, potentially more expensive for smaller teams.
  • The depth of auto-instrumentation might generate a large volume of data, requiring careful management.

• Pricing: Dynatrace offers a free trial, with paid plans based on consumption (e.g., host units, data ingest).

Grafana

Grafana, with its open-source core, provides unparalleled flexibility for visualizing and analyzing telemetry data from AI agents. When combined with managed services like Grafana Cloud (Loki for logs, Mimir for metrics, Tempo for traces), it becomes a powerful, customizable observability solution for security.

• Best for:

  • Developers who prefer open-source solutions and require highly customizable dashboards for agent security metrics.
  • Integrating diverse data sources (logs, metrics, traces) from various agent components.
  • Teams building custom anomaly detection for agent behavior using machine learning add-ons.

• Pros:

  • Extremely flexible and extensible for visualizing any agent-related security data.
  • Large community support and a vast ecosystem of plugins and integrations.
  • Grafana Cloud offers managed services for logs (Loki), metrics (Mimir), and traces (Tempo), simplifying operations.

• Cons:

  • Requires more manual setup and configuration compared to opinionated commercial platforms.
  • Security-specific features for AI agents often need to be custom-built or integrated.

• Pricing: Grafana's core is free and open-source. Grafana Cloud offers a free tier with paid upgrades for increased usage and managed services.

Elastic (ELK Stack)

The Elastic Stack (Elasticsearch, Logstash, Kibana) is a robust solution for log management, search, and analytics, making it highly valuable for security monitoring of AI agents. Its recent advancements in vector search and AI-powered attack discovery further enhance its utility for agentic workflows.

• Best for:

  • Log-centric security analysis of agent interactions and system events.
  • Leveraging vector search for contextual threat detection in agent prompts and responses.
  • Teams already invested in the ELK Stack for general observability and security.

• Pros:

  • Powerful search and aggregation capabilities for analyzing vast amounts of agent log data.
  • Kibana provides flexible dashboards for visualizing security events and agent behavior.
  • AI-powered attack discovery and vector search capabilities are increasingly relevant for AI agent security.

• Cons:

  • Can be resource-intensive to manage at scale, especially the open-source version.
  • Requires significant configuration and expertise to build out comprehensive security monitoring for agents.

• Pricing: The core Elastic Stack is open-source and free. Elastic Cloud offers a free trial and paid plans based on resource consumption and features.

Security Scanning Tools for Agentic Workflows

While the above tools focus on runtime observability, securing AI agents also critically depends on robust pre-runtime and SDLC security. These scanning tools ensure the code, dependencies, and infrastructure supporting your agents are secure. For a broader view of AI-powered security scanning, check out Best AI Security Scanning Tools for Developers in 2026.

Snyk

Snyk is a developer-first security platform that helps secure the entire application lifecycle, including the components that make up an AI agent. It’s crucial for identifying vulnerabilities in the agent's code, dependencies, containers, and infrastructure.

• Best for:

  • Securing the open-source dependencies used by AI agents.
  • Scanning the agent's application code (SAST) for common vulnerabilities.
  • Ensuring the security of containers and Kubernetes configurations hosting agents.

• Pros:

  • Integrates directly into developer workflows (IDE, SCM, CI/CD).
  • Provides actionable remediation advice for identified vulnerabilities.
  • Covers multiple security domains: dependencies, code, containers, and IaC.

• Cons:

  • Can generate a high volume of alerts, requiring careful prioritization.
  • The free tier is limited, and enterprise features can be costly.

• Pricing: Snyk offers a free tier for individuals. Paid team and business plans provide expanded features and usage.

Semgrep

Semgrep is a fast, open-source static analysis tool that allows developers to write custom rules, making it highly adaptable for detecting security patterns specific to AI agent code, including potential prompt injection vectors in application logic.

• Best for:

  • Fast static analysis of agent codebases for security vulnerabilities.
  • Authoring custom rules to detect AI-specific security patterns (e.g., unsafe handling of LLM outputs).
  • Integrating security checks early into the CI/CD pipeline for agent development.

• Pros:

  • Extremely fast scanning, suitable for pre-commit or CI/CD integration.
  • Custom rule authoring with a simple YAML syntax allows for highly specific security checks.
  • Large community and 2000+ out-of-the-box rules cover many common issues.

• Cons:

  • Requires effort to write and maintain custom rules for AI agent-specific threats.
  • Primarily a static analysis tool; does not provide runtime observability.

• Pricing: Semgrep's core is open-source and free. Semgrep Cloud offers paid tiers with advanced features like vulnerability management and team collaboration.

Checkov

Checkov is a free and open-source static analysis tool focused on Infrastructure as Code (IaC) security. It's essential for ensuring that the cloud infrastructure and configurations where AI agents are deployed adhere to security best practices.

• Best for:

  • Scanning IaC (Terraform, Helm, CloudFormation) that provisions agent environments for misconfigurations.
  • Enforcing security policies on the infrastructure supporting AI agents.
  • Integrating IaC security checks into CI/CD pipelines.

• Pros:

  • Free and open-source, making it accessible for all developers.
  • Supports a wide range of IaC frameworks and cloud providers.
  • Over 1000 built-in policies for common security and compliance checks.

• Cons:

  • Focuses solely on IaC; does not scan application code or runtime behavior.
  • Requires integration into existing CI/CD workflows.

• Pricing: Checkov is free and open-source.

Terrascan

Terrascan is another free and open-source IaC security scanner, offering policy-as-code capabilities with OPA/Rego. It's valuable for enforcing security standards on the infrastructure that hosts AI agents, including Kubernetes clusters and container definitions. For more on container security, see Best AI Tools for Container and Docker Security in 2026.

• Best for:

  • Policy-as-code enforcement for IaC related to AI agent deployments.
  • Scanning Terraform, Kubernetes, Helm, and Dockerfile configurations for security issues.
  • Teams looking for flexible policy definition using OPA/Rego.

• Pros:

  • Supports OPA/Rego for highly customizable and expressive policy definitions.
  • Covers a good range of IaC and container orchestration technologies.
  • Easy integration into CI/CD pipelines.

• Cons:

  • Similar to Checkov, it's limited to IaC and doesn't cover application code or runtime.
  • OPA/Rego can have a learning curve for new users.

• Pricing: Terrascan is free and open-source.

Developer AI Assistant for Secure Agent Development

JetBrains AI Assistant

While not an observability or direct security scanning tool, JetBrains AI Assistant plays a critical role in the development of AI agents. Its ability to generate code and analyze context means developers must observe and secure the output it produces, and understand its own interactions.

• Best for:

  • Improving developer productivity while building AI agent code.
  • Generating secure code snippets and commit messages for agent projects.
  • Understanding the context of AI suggestions to prevent introducing vulnerabilities.

• Pros:

  • Deeply integrated into JetBrains IDEs, providing context-aware assistance.
  • Can help generate tests and documentation, improving overall code quality and maintainability.
  • Assists with understanding complex code, potentially highlighting areas for security review.

• Cons:

  • Generated code still requires thorough review for security vulnerabilities.
  • Reliance on AI for code generation can lead to a false sense of security if not properly validated.
  • Not an observability tool itself; its output needs to be secured and monitored by other tools.

• Pricing: JetBrains AI Assistant is available as a paid add-on for JetBrains IDEs, with a free tier/trial available.

Decision Flow: Choosing the Right Tools for AI Agent Security Observability

Selecting the right tools depends on your existing stack, team expertise, and specific security requirements for your AI agents.

• If you need comprehensive runtime observability for LLM interactions and agent behavior, with AI-driven anomaly detection: Choose Datadog or Dynatrace.
• If you need a unified observability platform with a strong AIOps focus and a generous free tier: Choose New Relic.
• If you prefer open-source solutions for highly customizable dashboards and managed services for logs/metrics/traces: Choose Grafana (especially Grafana Cloud).
• If your primary need is log-centric security analysis, powerful search, and vector search for AI applications: Choose Elastic (ELK Stack).
• If you need to secure the open-source dependencies and application code of your AI agent: Choose Snyk and Semgrep.
• If you need to ensure the security of the infrastructure (IaC) where your AI agents are deployed: Choose Checkov or Terrascan.
• If you're developing AI agents and want an assistant that helps write secure code within your IDE, but understand its output needs validation: Choose JetBrains AI Assistant.
• For a holistic approach combining runtime monitoring with pre-runtime scanning: Integrate a platform like Datadog or New Relic with security scanners like Snyk and Semgrep. For deeper insights into runtime security, consider our guide on 9 Best AI Agent Runtime Security Tools for Developers 2026.

Get started with Semgrep → Semgrep — Open-source core free; Semgrep Cloud paid tiers

Conclusion

Securing AI agentic workflows in 2026 demands a proactive and comprehensive strategy. By integrating robust observability platforms with specialized security scanning tools, developers can gain the necessary visibility to detect, prevent, and respond to threats unique to AI agents. The tools highlighted here provide the technical capabilities to monitor agent behavior, secure underlying infrastructure, and ensure the integrity of your AI-powered applications. As AI agents evolve, so too must our approach to securing them, making continuous observability and security scanning indispensable.

Frequently Asked Questions

What are the primary security risks for AI agents?

Primary security risks for AI agents include prompt injection (manipulating agent behavior), data exfiltration (agents leaking sensitive data through tool use), unintended actions due to non-deterministic behavior, supply chain vulnerabilities in external APIs/models, and traditional code vulnerabilities in the agent's implementation.

How do observability tools help secure AI agents?

Observability tools help secure AI agents by collecting and analyzing logs, metrics, and traces of agent interactions, decisions, and tool usage. They can detect anomalies, unusual API calls, suspicious data access patterns, and prompt injection attempts in real-time, allowing developers to identify and respond to security threats.

Are traditional security scanning tools still relevant for AI agents?

Yes, traditional security scanning tools are highly relevant. They secure the underlying components of an AI agent: the application code, open-source dependencies, containers, and Infrastructure as Code (IaC) that define and host the agent. These tools help prevent vulnerabilities from being introduced into the agent's environment.

What is "LLM Observability" and why is it important for AI agent security?

LLM Observability refers to the specific monitoring of Large Language Model (LLM) interactions, including prompts, responses, token usage, and latency. It's crucial for AI agent security because it allows detection of prompt injection attacks, monitoring for sensitive data in prompts/responses, and understanding how the LLM influences agent behavior, which can have security implications.

Can AI assistants like JetBrains AI Assistant introduce security risks?

While AI assistants boost productivity, they can introduce security risks if their generated code is not thoroughly reviewed. The AI might produce code with vulnerabilities, or its suggestions could be misinterpreted, leading to insecure implementations. Therefore, the output of AI assistants must be validated and secured using other tools and practices.

How can I implement a holistic security strategy for my AI agents?

A holistic security strategy for AI agents involves combining pre-runtime security scanning (for code, dependencies, IaC) with runtime observability (for agent behavior, LLM interactions, tool use). This allows you to prevent vulnerabilities from entering the system and detect anomalous or malicious behavior during operation.