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As companies use AI agents to think, act and initiate workflows, it’s imperative to develop a plan to monitor and manage them.
When various components of an AI system begin making their own decisions, observability alone isn’t enough to ensure that operations will stay stable, safe or dependable.
In order to effectively manage AI agents throughout the enterprise, businesses must bridge the divide between problem identification and action. That goes beyond simply observing problems; businesses must actively prevent them.
The emergence of autonomous agents
The initial wave of enterprise AI was prompt-based systems; a user posed a query, the model responded and the exchange ended there. Although these early technologies were essentially reactive, they were helpful for search, copilots, content creation and summarization.
The subsequent wave is different. Not only do autonomous AI agents react, they also reason across objectives, select tools, extract information, take action and initiate workflows. They sometimes work in tandem with other agents or systems and increasingly serve as operational players within the company rather than as an interface layer for human instructions.
That change is significant because it affects the operational characteristics of AI. Teams are no longer solely keeping an eye on model outputs. Instead, they’re managing dynamic systems that can instantly affect clients, staff, infrastructure, business processes and other applications.
The powers of agents today
Agents’ talents evolve along with them. Agents can select what to do next, break down a goal into steps and complete activities at various levels. By contacting APIs, querying databases, searching internal systems, updating records and initiating downstream actions, they coordinate workflows. By integrating prompts, memory, business rules, retrieved information and real-time operational signals, agents can also make context-based judgments.
More sophisticated agents can identify when a workflow is failing, try again, escalate problems or forward jobs to a human reviewer. Within CRM, ticketing, cloud infrastructure, internal knowledge bases, observability platforms and business applications, agents can function independently. We anticipate that these skills will continue to expand rapidly.
How businesses are integrating autonomous AI agents
Agents are being incorporated into an expanding range of organizational operations, and they’re getting closer to operational processes where speed, accuracy, safety and governance are important. Some of those operations include: customer service and case handling, incident response and IT operations, workflows for DevOps and site dependability, code correction and software development, operational and supply chain planning, and more.
Emerging operational threats
However, as agents become increasingly independent, businesses must deal with a new kind of operational risk.
- Bad choices aren’t merely advised; they’re often carried out
- Minor mistakes can quickly spread to other linked systems
- Real-world actions can be triggered by hallucinations
- Agents may stray from business intent, policy, or compliance
- Interactions between multiple components can result in failures
- Automated decision-making can make decisions faster than human evaluation
While teams may observe symptoms, they must also be able to understand the reasons behind the system’s behavior. Enterprise AI needs dependability controls in addition to visibility.
AI systems’ complexities
Today’s AI-driven systems are rarely a single model. They’re distributed, layered systems made up of many interacting components that include:
- Foundational models (LLMs)
- Fine-tuned or task-specific small language models (SLMs)
- Embedding models
- Vector databases
- Retrieval pipelines and RAG components
- Prompt templates and prompt orchestration layers
- Training and evaluation datasets
- Guardrails and policy layers
- Agents and workflows
- Tool-calling systems
- Telemetry (aka logs, metrics and traces)
- Human-in-the-loop approval checkpoints
Their risks
Every component adds a different failure mode, and the way they interact adds further complexity. Even if a system appears to be strong at the infrastructure layer, it might still make bad choices and generate satisfactory results; all while building up operational risk below the surface.
Some of the associated risks include: the introduction of poor or corrupted inputs by data pipelines, infrastructure bottlenecks that reduce dependability, harmful or erroneous results, and operational bottlenecks in response to human review. Further complicating issues, systems with multiple agents or steps may fail in ways that aren’t immediately apparent.
AI observability
Traditional monitoring is insufficient to understand prompt behavior, retrieval quality, model drift, agent execution channels or the connection between AI behavior and downstream business or operational impact.
That’s where AI observability comes in. AI observability enables teams to understand how AI systems function in production by gathering, correlating and evaluating inputs and outputs, desired behaviors, and decision signals generated by those systems. That’s essential, because AI systems are dispersed, non-deterministic, and extremely context-sensitive.
AI observability offers end-to-end insight into AI workflows, so teams that utilize it can understand how prompts, models, retrieval layers, tools, and downstream systems interact during execution.
AI observability makes it possible to monitor performance and behavior, including latency, cost, token usage, throughput, error rates, model behavior and output quality indicators. It traces and analyzes execution-paths in complex agent workflows and demonstrates how results are reached across several steps and dependencies.
AI observability also finds anomalies across operational and AI signals by exposing anomalous behavior in models, pipelines, infrastructure or user-facing outcomes before teams discover them manually. It speeds up diagnostics when something goes wrong and makes root cause investigations easier by including AI-specific operations into your system telemetry (logs, metrics, traces and events).
Observability alone is not enough
Despite being an essential business practice, AI observability has inherent limitations.
Observability is diagnostic rather than preventive; teams can learn what went wrong but not necessarily how to stop it from happening again. It’s important to understand that knowledge of an agent’s past actions doesn’t automatically translate into control over the agent’s future actions.
When it comes to complex non-deterministic systems, observability can often overwhelm teams with data that leads to uncertainty. Rather than offering an operational answer, observability frequently ends at an explanation. Even while teams are aware of a problem, they may not have the automation, safeguards and control loops necessary to take corrective action.
That creates an operational gap. Businesses may be able to spot drift, poor results, dangerous behavior or degraded productivity, but they might still be unable to stop it from happening again, mitigate its effects, or maintain autonomous systems within safe operating parameters.
This means teams continue to operate reactively. They use manual intervention when something breaks, look into incidents after the fact, and rely on human labor to make up for systems that are becoming faster and more autonomous.
An overview of AI reliability
AI reliability goes beyond just observing issues. It’s the discipline of ensuring that AI systems function safely, consistently, predictably, and successfully in real-world production contexts. AI reliability comprehends and manages the complete system of systems around AI. It closes the loop between detection and action.
AI reliability focuses on whether the entire AI-driven system can function within reasonable operational constraints over time, rather than just if a model provided an accurate response. Quality, safety, resilience, explainability, policy compliance, cost effectiveness, and operational stability are all part of the equation.
The transition from detection to prevention
AI reliability reduces the time between recognizing a problem and handling its fix. It shifts the conversation from “what went wrong?” to “how quickly will our AI improve?” Employing the following techniques moves observability from passive observation to proactive prevention:
- Correlating signals across models, data and infrastructure to identify problems
- Proactive problem detection prior to impact
- Verifying all inputs and outputs in probabilistic AI systems to spot subtle behavioral changes
- Creating a feedback loop between detecting undesirable output in production and using that to generate fine-tuning data that improves the accuracy of underlying models
- Multi-agent workflow tracing to ensure you can connect the dots on why and how data evolved to inform complex actions
- Defined human-in-the-loop agentic workflows for safe response and automated remediation
Closing the gap between control and observation
Businesses benefit from frameworks that integrate visibility and control and require more than just an observability layer on top of generative AI. In both deterministic and non-deterministic systems, a reliability platform can identify, anticipate, explain, and assist in controlling problems.
The following should be included in a viable framework for dependable AI operations:
- Integrated telemetry for both IT systems and AI systems
- End-to-end agentic workflow and system dependency tracking
- AI-specific behavior and quality tracking (prompts and evals)
- Advanced anomaly detection, regardless of the source
- Causal reasoning and root cause analysis
- Alerting that automatically adapts to your environment and doesn’t require manual thresholds
- Policy enforcement and guardrails
- Human-in-the-loop evaluation of delicate or significant actions
- Automation of workflows and coordination of remediation
- Using predictive analysis to prevent issues from happening
- Feedback loops that connect anomaly detection with improved AI model quality
Facilitating AI functions
AI systems rely on infrastructure, services, data pipelines and operational routines; they don’t fail on their own. Teams get the whole picture when both AI and IT reliability are combined.
A thin LLM wrapper shouldn’t be the foundation of a trustworthy platform. To identify and fix problems that other generative AI-only tools overlook, a variety of AI techniques should be considered, including unsupervised AI, predictive AI, causal AI and generative AI. This blend of techniques is commonly known as “composite AI.”
Generative AI is good at summarizing natural language. It’s best suited for situations that require reasoning through unstructured data or when interacting with humans. But that doesn’t fit the shape of most reliability issues in production.
Predictive AI focuses on identifying early signals before they become outages, poor customer experiences or expensive failures by using anomaly detection algorithms.
Causal AI helps determine true root causes to reveal if retrieval quality, model behavior, infrastructure slowness, upstream data drift or downstream system failure was the cause of a performance decline.
Unsupervised AI autonomously uncovers hidden patterns, structures, or anomalies in data without human guidance. It outperforms generative AI for reliability because it focuses on finding hidden structures within complex, unclassified data to group similar items or find relationships.
When risk, ambiguity, or business effects are significant, operational AI agents must be able to automate reaction while maintaining human involvement for reliable operations.
The AI model’s comprehension of the particular business context can be enhanced in each encounter by using reinforcement learning from actual user data in production.
Even the most advanced systems go beyond alerting; closed-loop remediation learns from each incident over time, automates recognized reactions, and initiates safe measures.
Preparing for autonomous AI systems
Businesses can prepare for autonomous AI systems in a few different ways. First, agents should be viewed as operational systems rather than as instruments for productivity. Once an agent has the ability to act, it becomes an integral part of the business’s operations and should be regulated appropriately.
Teams may record signals from models, prompts, tools, workflows, infrastructure and user results right away by instrumenting agents. This basic surveillance cannot and ought not to be postponed until agents become essential to the business.
Establishing dependability standards prior to the widespread deployment of agents is also crucial. Instead of being introduced after the fact, acceptable thresholds for safety, latency, error rates, hallucination risk, policy compliance and business impact should be incorporated into their design.
Linking AI behavior to the underlying systems and procedures that support it lets businesses integrate AI and IT operations. Using different tools for infrastructure and for model monitoring creates blind spots.
Platform engineering, SRE, security, data teams, AI teams and business owners must work together to provide reliable AI operations, and autonomous systems transcend conventional silos.
Every incident, anomaly and near-miss will enhance the system by incorporating feedback loops into operations, allowing businesses to continuously learn from production behavior.
Lastly, it’s critical to select platforms that are intended for control rather than just observation. Businesses will gain from systems that integrate observability, prediction, explanation and action as AI agents grow more autonomous. Organizations that successfully transition from identifying problems to safely controlling outcomes will be the winners.
The bottom line
AI in businesses is now an operational system in enterprise environments rather than a tool. In real-world production settings, adding reliability to AI systems guarantees safe, consistent, predictable and efficient operations.
