What is AI Monitoring: Tools, Metrics & Best Practices

AI systems are rapidly becoming the backbone of modern enterprises. From customer support agents and recommendation engines to automated decisioning and internal copilots, AI now shapes how organizations compete, operate, and scale.

Yet as AI adoption accelerates, visibility has fallen behind. The challenge is no longer deploying models or agents. It is knowing what AI is running, what data it is touching, how it behaves at runtime, and when it fails or crosses risk boundaries. According to industry surveys, 51 percent of organizations have already deployed AI agents and another 35 percent plan to within two years. At the same time, security and compliance are cited as the number one barrier to scaling AI by 37 percent of enterprises. The result is friction where it matters most: 32 percent of AI initiatives stall at proof of concept, while competitors that solve security and monitoring early move faster and capture market share.

The cost of poor visibility is material. The average AI related breach now costs $4.8 million, with 82% of enterprises reporting sensitive data access by AI agents and 58% saying it occurs daily. Without runtime monitoring, breaches can go undetected for more than 200 days, amplifying regulatory penalties and customer churn. Beyond breaches, the absence of AI monitoring drives up manual oversight, compliance overhead, and operational drag, erasing the productivity gains that AI promises.

In a market where adoption is the default and trust defines differentiation, discovering AI failures after customers, regulators, or the board do is unacceptable. AI monitoring closes this gap by delivering continuous, real time visibility into model behavior, agent actions, data access, and security posture. It enables organizations to detect risk early, enforce guardrails at runtime, and scale AI with confidence before failures translate into lost revenue, lost trust, or strategic irrelevance.

What is AI Monitoring?

AI monitoring is the continuous, runtime observation of how AI systems behave across their full lifecycle, from development and testing to live production use. It gives security, engineering, and risk teams a real-time view of how models, agents, and AI workflows actually operate, not how they are expected to behave on paper.

Modern AI monitoring goes far beyond tracking model accuracy or uptime. It observes agent actions, model inputs and outputs, tool and API calls, data access patterns, identity usage, and decision paths. This allows teams to detect hallucinations, policy violations, sensitive data exposure, privilege misuse, model drift, and abnormal behavior that may signal security threats or systemic failure.

Traditional monitoring stops at logs and aggregate metrics. Runtime aware platforms like Levo.ai extend monitoring into the AI control plane itself, tracing agent to agent interactions, LLM calls, MCP servers, and downstream systems to expose transitive risk, hidden data flows, and unsafe execution paths. This is AI monitoring designed for agentic systems, where behavior emerges from chains of decisions, not single model calls.

Why AI Monitoring Matters?

AI systems are no longer experimental side projects. They sit directly in revenue paths, decision loops, and customer interactions. When AI fails, the impact is immediate and systemic. A single hallucinated response, unsafe action, or leaked data point can trigger regulatory exposure, customer churn, or a complete shutdown of AI programs.

Industry data shows why monitoring cannot be optional. Over 50% of enterprises already run AI agents in production, and 82% report that agents access sensitive data, with more than half seeing this daily. At the same time, the average AI related breach costs $4.8 million and often goes undetected for months without runtime visibility. In this environment, shipping AI without monitoring is equivalent to deploying critical infrastructure blind.

Continuous AI monitoring shifts organizations from reactive damage control to proactive risk management. It provides assurance that AI systems behave as intended, stay within policy, and adapt safely as models, prompts, tools, and data evolve. AI monitoring is no longer about observing models. It is about protecting the business systems, data, and trust that AI now controls.

Here’s why AI monitoring matters:

• Reliability: AI agents orchestrate workflows across systems. Monitoring catches failures, drift, and unsafe actions before they disrupt operations or customers.
• Security: Detect prompt abuse, data leakage, identity misuse, and anomalous agent behavior early, before they escalate into breaches.
• Compliance: Maintain audit ready evidence of AI behavior, data access, and decision paths aligned with emerging regulations and internal policies.
• Trust and Brand Protection: Identify hallucinations, bias, and unsafe outputs that can erode customer confidence in seconds.
• Operational Efficiency: Give engineering and security teams real time visibility to fix issues continuously instead of firefighting incidents after damage is done.

How AI Monitoring Works

AI monitoring works by continuously observing how AI systems behave at runtime, not how they are supposed to behave on paper. Instead of relying on static evaluations or periodic audits, monitoring instruments the live execution of models, agents, tools, and data flows to detect risk as it emerges.

At its core, AI monitoring combines runtime telemetry, behavioral analysis, and policy enforcement to create a live control loop. Every prompt, model response, tool invocation, API call, and data access is evaluated in context. This makes it possible to detect failures, misuse, or drift before they propagate into customer impact or regulatory exposure.

Modern AI monitoring goes far beyond output quality checks. Platforms like Levo.ai monitor the entire AI control plane in real time, including agents, LLM applications, MCP servers, APIs, identities, and sensitive data flows. By operating at runtime, monitoring captures what actually happens in production, even in complex multi-agent systems where risk emerges across chains, not single calls.

• Continuous, Runtime Aware Observation: AI monitoring instruments live execution paths to observe agent decisions, model outputs, tool usage, and downstream effects. This enables early detection of hallucinations, unsafe actions, prompt abuse, and behavioral drift that static testing cannot catch.
• Identity and Access Visibility: Monitoring tracks which identity authorized an action and which component executed it. This is critical in agentic systems where machine identities interact autonomously and privilege misuse can occur without human involvement.
• Policy Driven Enforcement: AI behavior is continuously evaluated against organizational policies such as data access limits, allowed actions, model usage constraints, and regulatory rules. Violations are flagged or blocked in real time, not discovered weeks later in audits.
• End to End Chain Awareness: In multi-agent workflows, monitoring correlates activity across agents and tools to surface transitive risks like privilege aggregation, context leakage, and confused deputy scenarios. Governance applies to the full chain, not isolated components.
• Low Friction, Always On Coverage: Modern platforms operate out of band, without code changes or performance impact, enabling continuous monitoring across development, staging, and production. Security stays enforced even as models, prompts, and integrations change daily.
• Actionable Signals, Not Noise: Effective AI monitoring validates real impact before raising alerts. Findings are tied to concrete behavior, data movement, and policy violations, allowing teams to respond quickly and confidently.

In practice, AI monitoring turns governance from a static checklist into a live safety system. It ensures AI systems remain reliable, secure, and compliant as they evolve, scale, and operate autonomously in production.

Metrics to measure AI Monitoring 

In an AI driven enterprise, models and agents increasingly make decisions that affect customers, data, and critical operations. When these systems fail, the impact isn’t limited to degraded performance, it can mean regulatory violations, financial loss, or loss of trust. 

Measuring AI health isn’t about abstract model scores; it’s about understanding risk, accountability, and real world impact. The right metrics give engineering, security, risk, and business teams a shared language for governing AI at scale.

Here are the key AI monitoring metrics that matter, and what they mean for business impact:

1. Output Quality and Hallucination Rate: For Trust and Accuracy

Tracking invalid, unverifiable, or nonsensical outputs reveals when models drift or operate outside intended bounds. Even a small rise in hallucination rates in customer facing or decision making workflows can erode trust and lead to incorrect actions, refunds, or legal exposure.

2. Policy Violation Rate: For Governance Effectiveness

This measures how often AI outputs or actions violate defined safety, security, or compliance policies. Spikes indicate gaps in prompts, guardrails, or access controls. A declining violation rate shows governance controls are actually working, not just documented.

3. Prompt Injection and Abuse Attempts: For Security Posture

Monitoring the frequency and success rate of prompt injection or jailbreak attempts helps teams assess how exposed AI systems are to manipulation. An increase may signal active attacks or emerging abuse patterns that require immediate mitigation.

4. Privilege Usage and Escalation Events: For Accountability

This tracks which identities AI agents use, what permissions are exercised, and whether privilege escalation occurs across agent chains. Unexpected privilege combinations are early indicators of systemic risk and a leading signal for potential breaches.

5. Sensitive Data Access and Exposure: For Compliance Readiness

Measuring how often PII, financial, or regulated data is accessed, transformed, or propagated by AI systems is critical for privacy compliance. Unauthorized or unnecessary access directly correlates with audit failures and regulatory fines.

6. Agent Action Failure Rate: For Operational Reliability

This captures failed tool calls, rejected API actions, retries, and partial executions by AI agents. Rising failure rates often precede broader system instability, broken workflows, and downstream outages.

7. Cross Agent Chain Risk Score: For Systemic Risk

In multi-agent environments, risk accumulates across chains. Monitoring chain depth, trust relationships, and data sensitivity produces a composite risk score that highlights fragile or over privileged workflows before they collapse.

8. Mean Time to Detect and Respond (MTTD / MTTR): For Business Resilience

How quickly teams detect and mitigate AI issues determines real impact. Lower MTTD and MTTR directly reduce blast radius, customer impact, and remediation costs.

By linking these metrics to business KPIs, such as policy violations to compliance risk, hallucinations to customer satisfaction, and privilege misuse to breach probability, organizations turn AI monitoring into a strategic control layer.

Continuous AI monitoring doesn’t just observe models; it quantifies risk, enforces accountability, and ensures AI systems remain trustworthy as they scale.

Key Benefits of AI Monitoring

In an enterprise where AI agents increasingly make decisions, automate workflows, and interact directly with customers, blind spots carry real business risk. Model failures, silent drift, or unchecked agent behavior can lead to regulatory exposure, financial loss, and rapid erosion of trust. As AI adoption accelerates, monitoring becomes the difference between controlled scale and systemic risk.

Modern AI monitoring goes beyond model accuracy metrics. It delivers continuous visibility, behavioral analysis, and policy enforcement across models, agents, data flows, and integrations. The outcome is not just safer AI, but sustained business confidence and operational control.

Here are the key benefits of effective AI monitoring:

• Trustworthy AI Outputs: Continuously validate model responses for accuracy, relevance, and hallucinations. Early detection of output degradation prevents customer facing errors, misinformation, and costly downstream decisions.
• Proactive Risk Detection: Identify abnormal agent behavior, prompt injection attempts, privilege misuse, or unsafe decision paths in real time. Behavioral baselines help distinguish genuine threats from normal operational variance.
• Reduced MTTD and MTTR: Automated alerts and contextual signals shorten mean time to detect and respond to AI failures. Teams can trace issues back to prompts, models, agents, or data sources quickly, limiting blast radius and recovery cost.
• Stronger Security Posture: Monitor sensitive data access, cross agent interactions, and tool usage continuously. Detect unauthorized data exposure or escalation paths before they become breaches.
• Regulatory and Audit Readiness: Maintain audit ready records of AI decisions, data usage, and policy enforcement. Simplify compliance with evolving regulations like the EU AI Act, DPDP Act, HIPAA, and sector specific governance requirements.
• Controlled AI Scaling: Ensure governance keeps pace as AI usage expands across teams and workflows. Monitoring prevents policy drift and inconsistent controls as new models, agents, and integrations are deployed.
• Operational Reliability: Track agent execution failures, retries, and dependency health to prevent cascading workflow breakdowns. Stable AI systems translate directly into predictable business operations.
• Improved Team Productivity: Replace manual reviews and ad hoc checks with continuous oversight. Engineers, security, and risk teams focus on real issues instead of chasing false positives or post-incident analysis.
• Preserved Brand Trust: By catching hallucinations, bias, and unsafe outputs early, organizations protect customer confidence in AI powered experiences, especially in high stakes domains like finance, healthcare, and retail.

Effective AI monitoring shifts organizations from reactive damage control to proactive governance, ensuring AI systems remain secure, compliant, and trustworthy as they scale.

Challenges in AI Monitoring

With AI systems expanding across agents, models, tools, and data sources, AI visibility challenges multiply quickly. Unlike traditional software, AI behavior is probabilistic, stateful, and highly context dependent. The result is that many organizations operate AI in production without truly understanding how decisions are made, where data flows, or when risk is introduced. 

The stakes are high: 79% of senior executives report active adoption of AI agents, yet 32% of AI initiatives stall at proof of concept, most often due to unresolved security and governance concerns. In practice, teams that solve AI monitoring and security first scale faster and capture market share, while others fall behind.

Effective AI monitoring requires deep, runtime context without slowing systems or violating privacy. Achieving that balance remains difficult for most enterprises.

Key challenges of AI Monitoring include:

• Opaque Decision Paths: LLMs and agents don’t follow deterministic logic. Without execution traces and reasoning context, it’s hard to explain why a model produced a specific output or took a particular action.
• Agent Sprawl and Shadow AI: Teams deploy agents, plugins, and tools rapidly, often outside central oversight. Untracked agents introduce hidden data access, policy violations, and unmanaged risk.
• Encrypted and Abstracted Workflows: AI interactions often occur behind encryption layers, SDKs, or orchestration frameworks, masking real behavior and data movement from traditional monitoring tools. As a result, 82% of AI enabled enterprises report sensitive data access by agents, with 58% saying it happens daily.
• High Monitoring Overhead: Inline instrumentation, prompt logging, or model-level hooks can add latency, increase costs, or disrupt user experience, making teams reluctant to monitor deeply. On average, AI-related breaches go unnoticed for 200+ days, amplifying impact and regulatory exposure.
• Alert Noise from Non-Determinism: AI outputs naturally vary. Static thresholds generate false positives, overwhelming teams with alerts that lack actionable context. Instead of reducing workload, lack of intelligent monitoring increases manual review, eroding the 40–50% productivity gains early AI adopters report.
• Fragmented Tooling: Model metrics, infrastructure logs, security signals, and compliance evidence live in separate systems. Without correlation, root cause analysis becomes slow and manual.
• Data Privacy and Compliance Risk: Monitoring that captures prompts, responses, or full payloads expands regulatory exposure under frameworks like the EU AI Act, India’s DPDP Act, and HIPAA extensions and increases the risk of sensitive data leakage. Enterprises without AI-native monitoring resort to manual controls and duplicate audits, wiping out margins and neutralizing AI ROI, even in cases reporting 200%+ returns.
• Governance Drift at Scale: With AI models evolving and agents chaining together, policies that were valid at deployment time quickly become outdated, leaving gaps between intent and enforcement.

Overcoming these challenges requires AI monitoring that is runtime aware, policy driven, and privacy preserving, designed specifically for the complexity and speed of modern AI systems.

How Levo Solves these AI Monitoring Challenges as the one stop AI Monitoring Solution

Levo.ai addresses the hardest AI monitoring problems by shifting visibility to where AI systems actually operate: at runtime, across agents, tools, identities, and data flows. Instead of relying on static logs, prompt sampling, or post-hoc audits, Levo delivers continuous, high-fidelity insight without slowing systems or expanding compliance risk.

• Complete runtime visibility across AI systems: Levo monitors live AI behavior across agents, LLM apps, MCP servers, APIs, and tool calls. Every action is observed in context, so teams know what the AI did, which tools it used, what data it accessed, and how decisions propagated across multi-agent workflows.
• Agent to agent and tool chain tracing: Levo traces interactions across agent chains, uncovering transitive trust, privilege aggregation, and hidden execution paths that traditional monitoring misses. This eliminates blind spots created by autonomous handoffs between agents.
• Identity aware accountability: Every AI action is mapped to identity and authorization context, showing who approved an action, which agent executed it, and under what permissions. This brings auditability and ownership to machine to machine interactions, a critical gap in most AI environments.
• Policy as code enforcement in real time: Levo enforces declarative security and governance policies directly on live AI workflows. Policies propagate across entire agent chains, ensuring sensitive data access, tool usage, and actions remain compliant even as systems evolve.
• High signal risk scoring, not alert noise: Levo prioritizes issues using contextual risk scoring based on data sensitivity, privilege mix, execution depth, and blast radius. Teams focus on what matters most instead of chasing false positives from non-deterministic AI behavior.
• Zero disruption, zero payload exposure: Monitoring runs out of band, without inline proxies, prompt interception, or model hooks. Sensitive prompts and responses stay in the customer environment, minimizing compliance scope while preserving deep behavioral insight.
• Built for scale and AI speed: From single agents to complex, enterprise wide agentic systems, Levo scales with AI adoption. AI Governance never lags deployment, preventing pilots from stalling and enabling secure AI rollout across production workflows.

By combining runtime observability, identity context, and policy enforcement, Levo.ai turns AI monitoring from a reactive safety net into a proactive control plane, removing blind spots, reducing risk, and allowing enterprises to scale AI with confidence.

Types of AI Monitoring

AI monitoring spans multiple layers of the AI lifecycle, from model behavior to agent execution, data usage, and security posture. Each monitoring type captures a different failure mode, and together they provide the coverage needed to operate AI safely at scale.

1. Model Behavior Monitoring: Tracks how models perform in production, including response quality, hallucination rates, toxicity, bias signals, and output consistency. Helps teams detect silent degradation, prompt drift, or unexpected model behavior before it impacts users or decisions.

2. Agent and Workflow Monitoring: Observes how autonomous agents plan, call tools, hand off tasks, and coordinate across multi-agent systems. Exposes execution paths, decision chains, and failure points that emerge only at runtime, especially in agentic workflows.

3. Data and Privacy Monitoring: Monitors how sensitive data is accessed, transformed, and propagated through prompts, tools, and downstream systems. Detects unapproved PII usage, policy violations, and risky data flows that can trigger compliance or trust failures.

4. Security and Access Monitoring: Focuses on identity, permissions, and execution context. Flags excessive privileges, unauthorized tool usage, token misuse, and transitive trust issues across agents and services—key risks in autonomous AI environments.

5. Performance and Reliability Monitoring: Tracks latency, tool-call failures, retries, timeouts, and dependency health. Ensures AI systems remain responsive and reliable as traffic scales and workflows become more complex.

Unified AI Monitoring with Levo.ai
Levo.ai brings these monitoring layers together into a single, runtime informed control plane. By correlating model behavior, agent execution, identity, data access, and performance in real time, Levo delivers complete AI observability without inline agents, payload capture, or operational drag, making AI monitoring practical, scalable, and secure.

Best Practices for Effective AI Monitoring

Effective AI monitoring is not just about observing models in production, it’s about maintaining control, trust, and reliability as AI systems evolve into autonomous, multi-agent workflows. With AI operating across data, tools, and decisions in real time, monitoring practices must be continuous, contextual, and security first.

Here are the best practices for effective AI monitoring:

1. Define Clear AI SLOs and Risk Thresholds: Set measurable objectives for latency, failure rates, hallucination tolerance, and data access risk. Tie thresholds to business impact so deviations trigger the right response, not alert fatigue.

2. Monitor Agent Identity and Execution Context: Track who authorized actions, which agent executed them, and under what permissions. This prevents silent privilege escalation and enforces accountability across machine to machine workflows.

3. Correlate Model Outputs with Actions and Tools: Isolated model metrics are insufficient. Link prompts, responses, tool calls, and downstream effects to understand how AI decisions translate into real world actions.

4. Detect Behavioral Drift in Real Time: Models and agents change behavior over time due to prompt updates, tool changes, or data shifts. Use behavioral baselines to catch hallucination spikes, unsafe responses, or workflow deviations early.

5. Enforce Privacy First Observability: Avoid capturing raw prompts or sensitive payloads. Monitor metadata, execution paths, and policy signals instead, preserving visibility without expanding compliance or breach risk.

6. Continuously Validate Policies at Runtime: Static reviews fail in dynamic systems. Enforce policies continuously across agents, tools, and data flows to ensure governance holds as AI systems scale.

7. Treat Monitoring as a Living System: AI evolves daily. Regularly update coverage, policies, and risk models to reflect new agents, tools, regulations, and attack patterns.

Done right, AI monitoring shifts teams from reactive audits to proactive control, keeping AI systems reliable, secure, and trustworthy as autonomy and scale increase.

Top AI Monitoring Tools

AI Monitoring tools provide continuous visibility into how AI systems behave in real time, across models, agents, tools, data flows, and production environments. They help organizations detect hallucinations, unsafe actions, data exposure, drift, and security risks before these issues impact customers, compliance, or revenue.

With AI systems moving from single models to autonomous and multi-agent workflows, monitoring becomes a control plane, not just an observability layer. The right AI Monitoring tools must operate at runtime, preserve privacy, scale across agents, and convert live telemetry into enforceable governance and security signals.

Here are the top AI Monitoring tools for 2025:

1. Levo

Levo.ai is a runtime first AI monitoring and governance platform built for agentic systems. Provides deep visibility across AI agents, MCP servers, LLM applications, APIs, and sensitive data flows using eBPF-based instrumentation. Detects hallucinations, privilege aggregation, transitive trust leaks, unsafe tool usage, and policy violations in real time, without ingesting sensitive payloads.

2. Arize AI

Focuses on model performance monitoring, drift detection, and evaluation. Strong in ML observability and post deployment analytics for traditional ML and LLMs, but limited runtime enforcement for agent workflows.

3. Fiddler AI

Specializes in model explainability, bias detection, and performance monitoring. Well suited for regulated industries needing explainability, though primarily model centric rather than agent centric.

4. WhyLabs

Open source oriented platform for data drift, model health, and LLM observability. Strong in statistical monitoring and anomaly detection, with limited native governance controls.

5. Datadog AI Observability

Extends infrastructure and application observability into AI workloads. Useful for correlating AI latency and errors with infrastructure metrics, but less focused on agent security and data governance.

6. New Relic AI Monitoring

Provides performance monitoring for AI powered applications and integrations. Strong APM heritage, with emerging support for LLM telemetry and tracing.

7. LangSmith (LangChain)

Developer focused tool for tracing, debugging, and evaluating LLM applications built with LangChain. Effective for development workflows, but not designed for enterprise scale governance.

8. OpenAI Evals / Custom Tooling

Used for offline evaluation and testing of model behavior. Valuable for experimentation, but lacks continuous production monitoring and enforcement.

9. Azure AI Monitor / AWS Bedrock Monitoring

Cloud native monitoring tied to specific ecosystems. Useful for platform level metrics, but limited cross cloud and multi-agent visibility.

10. Splunk (AI + Security Analytics)

Strong in log aggregation and security analytics, often used as a downstream SIEM. Requires significant customization to support AI-specific monitoring use cases.

Together, these tools address different layers of AI observability. However, as AI systems become autonomous and interconnected, platforms like Levo.ai, which combine runtime visibility, policy enforcement, and security across agent chains, are emerging as the foundation for effective AI monitoring at scale.

Why Levo.ai Is the Right AI Monitoring Platform for 2026

Traditional monitoring tools stop at logs and metrics. They lack the runtime context needed to safely observe autonomous AI systems that act, decide, and interact with sensitive data in real time. As AI agents move into production, monitoring must be continuous, contextual, and security aware by design.

Levo.ai is built for this new reality.

• Runtime-First AI Monitoring: Levo monitors AI systems where risk actually emerges: at runtime. It observes live agent behavior, tool calls, API interactions, and data access patterns, instead of relying on offline evaluations or post-hoc logs.
• eBPF-Powered, Pre-Encryption Visibility: Levo uses eBPF to capture AI and API activity directly at the kernel layer, before TLS encryption. This delivers full behavioral visibility without agents, code changes, proxies, or performance impact.
• Zero-Payload, Privacy-Safe by Design: Sensitive prompts, responses, and data never leave your environment. Levo analyzes metadata and execution context only, dramatically reducing compliance scope and eliminating data leakage risk.
• Built for Agentic and Multi-Agent Systems: Unlike model-centric tools, Levo traces agent-to-agent communication, detects privilege aggregation, transitive trust leaks, unsafe tool usage, and confused-deputy patterns that emerge only in chained workflows.
• Policy as Code, Enforced in Real Time: Security and governance policies are enforced continuously across agents and tools. Controls don’t drift as systems evolve, scale, or reconfigure.
• High-Signal Risk Correlation: Levo doesn’t just alert. Every finding is enriched with context: agent identity, tool invoked, data touched, authorization state, and chain depth, enabling fast root-cause analysis and prioritization.
• Unified Monitoring, Security, and Governance: Performance, safety, security, and compliance signals converge into a single control plane. AI teams, security teams, and compliance leaders operate from one shared source of truth.
• Low Overhead, Enterprise Scale: Deploy in hours. Scale across thousands of agents, APIs, and environments with no agents, no proxies, and no operational drag.

In 2026, AI monitoring is no longer about visibility alone. It’s about control, accountability, and trust at runtime. Levo.ai delivers all three, making it the right AI monitoring platform for enterprises building with autonomous and agentic AI systems.

The Way Ahead: Implementing Robust AI Monitoring

AI systems are scaling faster than most organizations can safely observe. Every new agent, tool integration, or model update introduces hidden execution paths, data flows, and failure modes. In this environment, AI monitoring isn’t a feature, it’s the foundation of trust, safety, and scale.

Legacy monitoring was built for logs and metrics, not autonomous systems that reason, act, and interact with sensitive data. Static dashboards and post incident audits only explain failures after damage is done.

That’s where Levo.ai changes the equation.

Levo.ai brings runtime first AI monitoring powered by kernel-level telemetry, giving teams visibility into how AI agents actually behave in production, not how they were intended to behave. By observing agent actions, tool calls, API interactions, and data access in real time, Levo enables proactive detection of failures, misuse, and risk before they escalate.

With its eBPF based, zero overhead architecture, Levo monitors AI systems pre-encryption without agents, proxies, or payload ingestion. Sensitive data never leaves your environment, keeping privacy, compliance, and performance intact.

More importantly, Levo connects monitoring with control. Behavioral signals are correlated with identity, authorization, and policy context, allowing teams to understand who did what, using which privileges, and with what data, across single agents and complex multi-agent workflows.

Whether you’re deploying customer facing AI, automating internal workflows, or scaling agentic systems across the enterprise, Levo.ai helps teams move from reactive incident response to proactive assurance.

Monitor AI continuously. Detect risk early. Govern with confidence. That’s how Levo.ai defines AI monitoring for 2026.

Monitor AI in real time with Levo.ai. Book a demo to get started.