## The 3 AM Problem Nobody Talks About

Aaron Sneed runs a defense-tech company with 15 AI agents he calls "The Council." These agents handle everything from legal review to HR scheduling. They save him 20 hours every week. But here is the critical detail most people miss: **he had to build his own monitoring stack because nothing off-the-shelf worked.**

"I train them to push back on my ideas rather than just agreeing," Sneed told Business Insider in February 2026. "But that only works if I can see what they are actually doing."

Most teams do not have Sneed's background. They are trying to deploy agents on infrastructure built for static microservices. They are using Datadog dashboards designed for request latency, not agent reasoning chains. They are getting alerts about CPU usage when the real problem is an agent that decided to delete customer data because of a prompt injection.

The Reddit threads tell the whole story. Search r/devops for "Datadog pricing" and you will find the same horror story repeated dozens of times. One user reported their bill went from $2,000 to $20,000 per month after adding Kubernetes monitoring. Another said they were quoted $50,000 per month for 500 hosts with Dynatrace.

These tools were built for an era where monitoring meant tracking servers and APIs. They were not built for agents that make autonomous decisions, chain multiple tool calls, and maintain state across long-running sessions.

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## The Statelessness Trap

Here is what breaks every agent deployment eventually: **statelessness.**

Most agents reset their context window every session. They forget what they learned yesterday. They repeat mistakes they made last week. They are like hiring an employee with perfect amnesia who shows up every morning with no memory of your business.

A CrewAI survey released February 2026 found that 100% of enterprises plan to expand agent adoption this year. But the same survey revealed that integration difficulties (35%), ease of integration with legacy systems (30%), and reliability concerns (24%) are the top blockers.

The reliability problem is really an observability problem. When an agent fails at 3 AM, your on-call engineer needs to reconstruct what happened. What was the agent trying to do? What tools did it call? What was the LLM response at each step? What state did it have when it made that catastrophic decision?

Traditional APM tools give you none of this. They will tell you the agent API returned a 500 error. They will not tell you that the agent called the wrong tool because of a confusing prompt, or that it hallucinated a customer ID and tried to delete the wrong record.

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## The Session Replay Opportunity

The session replay market is projected to hit $323 million in 2026, growing to $724 million by 2035. 58-67% of session replay tools now include AI for automated detection of frustration signals like rage clicks and errors.

But here is the insight most people miss: **session replay for agents is completely different from session replay for users.**

When a human user clicks around your app, you can record their mouse movements and replay them later. When an agent interacts with your systems, there is no mouse. There is a chain of thought, a series of tool calls, LLM responses, and state changes.

You need to replay the agent's reasoning process, not its UI interactions. You need to see the prompt that went to the LLM, the response that came back, the tool the agent chose to call, and the result it received. You need to see this across multiple steps, potentially spanning hours or days.

This is why traditional session replay vendors are struggling to adapt. Their whole model is built around visual playback of DOM interactions. Agents do not have DOM interactions. They have API calls, database queries, and LLM completions.

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## Observability Becomes the Control Plane

Dynatrace Perform 2026 made this explicit. The company announced observability as an "agent OS" or control plane, with expansions in cloud telemetry and real user monitoring on their Grail platform.

Other vendors are following. Splunk launched AI Infrastructure Monitoring and AI Agent Monitoring in alpha. Azure AI Foundry built unified agent governance with tracing and evaluation. Braintrust, Maxim AI, and others are building agent-specific observability from the ground up.

The thesis is simple: **agents are too autonomous to manage with traditional monitoring.** You cannot watch every decision. You need systems that watch the watchers. You need meta-agents that monitor your agents, detect anomalies in their behavior, and intervene when they go off track.

This is observability as a control plane, not just a dashboard. It is the difference between watching a car crash on a traffic camera and having an autopilot system that takes control when the driver swerves.

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## The Real Cost of Getting This Wrong

Let me give you the numbers that matter.

A mid-size SaaS company running traditional observability is paying $10,000-$100,000 per month for Datadog or Dynatrace. These bills scale with data volume, and agents generate a lot of data. Every tool call, every LLM completion, every state change is a telemetry event.

Now add the cost of agent failures. A manufacturing company using predictive maintenance agents saw a 42% accuracy improvement. But that only works if the agents are running. When they fail silently, you are back to reactive maintenance and unexpected downtime.

Emad Mostaque, founder of Stability AI, put it bluntly: "We see the wave coming. Now this time next year, every company has to implement it --- not even have a strategy. Implement it."

But implementation without observability is reckless. You are flying blind in a thunderstorm. The crash is inevitable.

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## What the Winning Teams Are Doing Differently

I have talked to teams that are making this work. Here is what they have in common.

**First, they treat observability as infrastructure, not an afterthought.** They design their agent architecture with tracing built in from day one. They use OpenTelemetry for vendor-agnostic instrumentation. They capture the full agent execution context, not just the final output.

**Second, they invest in evaluation, not just monitoring.** Monitoring tells you when something broke. Evaluation tells you when something is about to break. They run continuous evals on live data, checking for quality drift, cost spikes, and latency degradation.

**Third, they build or buy agent-specific observability.** They do not try to retrofit Datadog to understand LangChain traces. They use tools like Braintrust, Langfuse, or Arize Phoenix that were built for the agent paradigm.

**Fourth, they implement governance from the start.** They have kill switches for rogue agents. They have policy engines that enforce guardrails in real-time. They treat agent identity and authorization as first-class concerns.

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## The Vendor Landscape Is Splitting

The observability market is bifurcating. On one side, you have the incumbents: Datadog, Dynatrace, New Relic. They are adding AI agent monitoring as a feature, but their core architecture was built for a different world. They charge by volume, which breaks at AI scale.

On the other side, you have the upstarts: Braintrust, Maxim AI, Langfuse, Arize. They are building agent-native observability from the ground up. They understand that an agent trace is not a request trace. They know that cost and quality matter as much as latency and error rate.

The session replay vendors are in a tough spot. 74% of providers now embed GDPR/CCPA-compliant features, and 67% offer cross-device support. But they are still thinking about human users, not AI agents.

The opportunity is massive. The team that builds the equivalent of FullStory or Hotjar for agent sessions will capture a huge chunk of this market.

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## The Bottom Line

Here is what nobody tells you: **the companies winning with AI agents are not the ones with the best prompts or the biggest models. They are the ones with the best observability.**

Aaron Sneed's 15 agents work because he can see what they are doing. He can debug their reasoning. He can catch failures before they cascade. He can iterate based on real production data.

Most teams do not have this. They are deploying agents into black boxes, hoping for the best, and getting surprised by 3 AM outages they cannot diagnose.

The session replay market is growing at 9.5% CAGR because companies know they need visibility into user behavior. The agent observability market will grow faster because companies are about to realize they need visibility into agent behavior even more.

If you are building with AI agents in 2026, you have two choices. You can invest in observability now, while you are still in pilot. Or you can wait until your first production incident costs you a customer.

One of these choices is expensive. The other is catastrophic.

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## Sources and Further Reading

- CrewAI Survey: "Agentic AI Reaches Tipping Point" (February 2026) - 100% of enterprises plan to expand agent adoption

- Gartner Predictions: 40% of enterprise applications will embed task-specific agents by end of 2026

- McKinsey Data: Only 23% of firms scaling past pilot

- Business Insider: "Solo founder runs company with 15 AI agents" featuring Aaron Sneed

- Market Growth Reports: Session replay software market projected at $323M for 2026

- Reddit r/devops: User reports of Datadog bills jumping from $2,000 to $20,000/month

- Futurum Group: Dynatrace Perform 2026 analysis on observability as "agent OS"

- Beam.ai: Enterprise AI agent trends and pain points research

- Splunk: AI Infrastructure Monitoring and AI Agent Monitoring announcement

- Groundcover: "Why volume-based observability fails at AI scale"

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*Want to monitor your agents properly? Start with tracing. Everything else builds from there.*