Gaurav Aggarwal, Senior Vice President at WinWire, Global Head Presales & Solutions Engineering.
The past decade has witnessed software changing from being rule-based automation to self-learning intelligence. Today, an era is beginning where AI agents are no longer simply reactive tools, but proactive colleagues.
These intelligent agents are able to reason, adapt and act autonomously. In global businesses, they’re already making decisions in customer service, cybersecurity, operations and more. Salesforce’s “Agentforce” model, for instance, empowers agents to independently resolve more than 80% of service cases—unleashing human teams on higher-order problem-solving.
This is not automation in scale. It’s strategic autonomy. And it’s bringing about what some call the Agentic Economy—a future where AI agents manage workflows end-to-end.
But with autonomy, there’s complexity. And complexity, left unmanaged, turns to risk.
The Evolution: MLOps, LLMOps And AgentOps
When enterprise AI was young, MLOps came along to oversee the machine learning model lifecycle—optimizing training, deployment and monitoring. With the rise of large language models (LLMs) to the forefront, LLMOps adapted to support prompt engineering, fine-tuning and scaling inference costs.
Today, we are on the cusp of a new field: AgentOps.
Contrary to LLMs that produce answers, AI agents autonomously take action, engage with systems and learn through experience. They need more than monitoring models; they need monitoring of behavior, processes and results.
AgentOps isn’t an evolution—it’s responsible scaling of intelligent agents.
What AgentOps Actually Means
AgentOps manages the complete life cycle of autonomous agents from design and deployment through monitoring, evolution and retirement. Fundamental to it is answering three questions of utmost importance:
• Are the agents executing what they are meant to?
• Can we understand how they reached their conclusions?
• Can we continuously optimize their performance in a safe manner?
This science is based on three pillars of strength:
1. End-To-End Observability
Observability is bigger than logs—it’s building a 360-degree picture of agent activity throughout the full decision loop.
• Inputs and Outputs: Monitor what agents ingest (user requests, APIs, environmental sensors) and what they cause (responses, transactions, escalations).
• Reasoning Logs: Record in-process thinking—important for regulated domains or mission-critical processes.
• Environment Interactions: Observe how agents engage with enterprise systems, people and even other agents.
Example: In online shopping, behavior dashboards monitor an agent’s decision reasoning behind tailored product suggestions and warn when intent deviations are detected.
2. Traceability And Accountability
When an agent is approving a transaction or indicating vulnerability, can you say why it did that?
• Decision Logs: Log the logical sequence, retrieved information and contextual signals culminating in a decision.
• Version Control: Track changes in prompts, logic or settings that might change behavior.
• Reproducibility: Re-create historical states—such as agent version, data inputs and environmental settings—to support auditability and compliance.
Example: JP Morgan employs deep traceability frameworks in its agent-based systems to guarantee that every financial decision adheres to rigorous regulatory requirements.
3. Deep Monitoring And Debugging
AI agents operate in workflows with APIs, third-party data and multistep chains of logic—complicating the debugging and monitoring.
• RAG Pipelines: Track what outside sources the agent draws from and ensure data freshness and relevance.
• Prompt Engineering Tools: Test and refine prompts through user contexts to enhance consistency.
• Workflow Debuggers: Display each step, from input to action and mark where logic breaks.
Example: Teams working on GitHub Copilot or agent-based IDEs now test edge cases with behavior test frameworks, monitoring performance through iterations.
Why This Moment Requires Action
It is easier than ever to build AI agents. Open-source libraries, API wrappers and plug-and-play solutions have accelerated deployment. Without structure, innovation becomes fragmentation.
• Shadow AI: Unmonitored agents operating beyond central control.
• Tool Sprawl: Different groups implementing rival workflows without coordination.
• Compliance Gaps: Unchecked actions lacking audit trails cry out to be altered.
AgentOps brings these risks under control by turning chaos into choreography—every agent operating safely, reliably and effectively.
Building Your AgentOps Function: Lessons I Learned
When we first deployed AI agents, I expected technical issues. The real challenge? Building operational trust at scale.
Lesson 1: Observability needs to be built from Day One—and not Day 90.
We deployed a service agent across regions. It cleared pilots, but flunked at scale—complaining about archaic policies in certain markets. The issue wasn’t the model. It was a lack of oversight.
That experience shifted my perspective on observability. It’s not about logs and dashboards—it’s deliberate clarity. You have to always be able to see what your agents are doing and why.
Lesson 2: Test for ambiguity, not accuracy.
We released a finance agent that alerted on real-time anomalies in spending. It tested successfully in the lab—but crashed in production because of edge-case tax laws between jurisdictions. It wasn’t buggy—it just hadn’t encountered real-world nuance.
Now, each agent we deploy is scenario-tested for ambiguity, gray areas and inimitable edge conditions. That’s where trust is established—or destroyed.
Lesson 3: You’re not deploying agents—you’re choreographing intelligence.
We once had four isolated agents handling segments of our data pipeline—ingestion, validation, enrichment, reporting. Alone, they worked. Together, they faltered. Handoffs were cumbersome. Logs did not match up. When they failed, tracing root causes between agents was hell.
That’s when the penny dropped: AgentOps is not about operating agents in isolation. It’s about orchestrating systems—where workflows are traceable across, not within, agents.
These lessons I learned firsthand that AgentOps is actually a discipline of leadership. It’s scaling autonomy without sacrificing control. It’s ending experimentation with AI—and beginning to operationalize it.
And most importantly, it’s how you keep agents from merely acting—but acting responsibly, transparently and in accordance with your mission.
Lead The Future Of Enterprise Intelligence
We’re no longer debating whether AI agents should be in the enterprise—because they already are. The question is: Are we ready to lead them?
AgentOps isn’t a platform. It’s an attitude. One that signals your business is ready not just to be open to autonomy but to develop it responsibly, strategically and transparently.
Because in the Agentic Economy, success won’t be determined by how many agents you possess—but by how well you manage them.
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