AI Agent Observability. Seeing What Went Wrong
The picture is becoming clearer: knowledge workers collaborating with LLM powered agents.
Over time, these agents will earn trust and win pre-approvals to make operational decisions that drive business outcomes—faster, cheaper, and at scale.
AI agents will evolve, operate across workflows, and leverage domain-specific LLMs for specific tasks (e.g. planning vs. data analysis vs. patching code).
Why? Better IQ, lower latency, optimized costs, and tighter security. Add local business knowledge (via RAG) to the mix, and you get grounded, reliable decision-making.
Elements of this are not far away. Now, fast forward to this scenario: an AI agent makes a decision that derails a critical business workflow.
You try to figure out what happened, only to discover:
No audit trail
No clues about what went wrong
No way to stop it from happening again
Operational Observability: A Critical Must-Have
To trust AI agents, you need to see what they’re doing, why they’re doing it, and when it goes wrong.
For cyber, that requires visibility to detect and respond to:
Prompt injection (adversarial manipulation)
Manipulated RAG responses (corrupted data or insider threat)
Anomalous outputs (unexpected or nonsensical decisions)
Operator error (bad prompts, misconfigurations)
Without robust instrumentation, you’ll fly blind - you won’t know what failed, where, or why. Interview the AI agent after the fact?!
The Winners Will Build Visibility Early
Companies experimenting with AI today are rightly worried about data privacy, legal risk, and protecting intellectual property. Some are stepping back and asking, “Where do we have AI in our supply chain today that we don’t know about?” Fair question.
The future winners will be those who prioritize visibility before small missteps snowball into costly failures. Tracing, logging, and securing key decision inputs, outputs and actions taken.
If you’ve worked in a regulated business, you already know this: explainability, transparency, and governance are non-negotiable for Key Controls.
LLM-Powered Agents: Moving Fast, Breaking Faster
AI providers will shift. Tools will evolve. New decision-making agent architectures will be dreamed up. But failures will stay locked in a black box - just like the LLMs they’re built on if that decision-making isn't instrumented.
Traditionally, cyber threat detection gets pushed to the back of the line. But now? With AI agent proof-of-concepts emerging, smart CISOs will spot the opportunity to get directly involved. They will task their security pros to ensure cyber visibility is baked into the emerging agent evaluation frameworks their businesses adopt.
Businesses may not have those frameworks yet, but they soon will.
Why? Businesses will need confidence that agents deliver on tasks, run efficiently, and don’t waste premium LLM tokens for low IQ tasks. Operational oversight through agent-specific task metrics won’t be optional as AI scales - it’ll become business-critical.
The question is: will security leaders step in early and influence the need for - and design - of AI agent evaluation frameworks?
Bottom line: When AI drives your workflows, observability drives your trust.
I’d love to hear your take. Hit reply and let me know.