Webinar: Controlled Chaos - How to Monitor and Improve LLMs in Production

Introduction – A Cambrian explosion in AI

In just the last two years we’ve gone from a handful of publicly‑available LLMs to thousands of fine‑tuned, open‑source, and proprietary variants; new vector databases, orchestration frameworks, and guardrail libraries land on Hacker News every week. The pace is reminiscent of Earth’s Cambrian period, when evolutionary experimentation produced an astonishing diversity of life in a geological blink. For builders it feels equally exhilarating—and equally chaotic.

This rapid speciation has two direct consequences for production teams:

1. Unstable ground rules. Techniques, licenses, and even model behaviors can change between the sprint planning meeting and the release candidate. “Best practice” is provisional.

2. Expanding risk surface. Every new capability (tool calling, long‑context windows, multimodality) introduces fresh failure modes that documentation hasn’t caught up with.

In other words, surprise is now the default. Because LLMs do not crash with stack traces—they produce plausible‑looking text—most failures are silent until a user points them out or, worse, acts on faulty output. That is why we need controlled chaos: observability loops strong enough to surface hidden breakage yet lightweight enough to keep pace with the field.

Large‑language‑model (LLM) apps often behave unpredictably once real users interact with them. Outputs can vary, external APIs can fail, and user requests can push the system into edge‑case territory you never saw in testing. The goal of controlled chaos is to accept that surprises will happen while putting guardrails, tracing, and rapid‑feedback loops in place so those surprises never become outages or reputational damage.

Why LLMs Derail

Understanding why LLMs derail is essential for effectively managing them. When deploying these models in the real world, several key factors can cause unexpected behaviors and challenges. Awareness of these factors allows teams to prepare robust strategies to detect and mitigate potential issues.

1. Probabilistic Outputs

LLMs are fundamentally probabilistic. The same prompt can yield significantly different answers due to inherent randomness in token generation. This unpredictability can surprise users and degrade their trust if not properly managed.

2. Unbounded User Behavior

Users will interact with LLM applications in unexpected ways. Jailbreak attempts, unclear or ambiguous phrasing, and extraordinarily lengthy inputs can cause models to respond unpredictably, risking compliance issues and user dissatisfaction.

3. Scale Multiplies Edge Cases

Once an LLM is deployed widely, the sheer volume of interactions amplifies rare edge cases. Issues undetectable during initial testing quickly become common when scaled to thousands of daily interactions, potentially overwhelming teams unprepared for production complexities.

Mindset Shift – Expect the Unexpected

Adopting a proactive mindset is crucial for successful LLM deployments. The old approach of striving for perfection before deployment no longer aligns with the dynamic and probabilistic nature of modern AI models. Instead, teams should expect the unexpected, creating strategies and infrastructure designed explicitly to handle uncertainty and quickly address unforeseen issues.

To foster this mindset, organizations must focus on deep instrumentation of their systems, ensuring comprehensive visibility into how the LLM performs in real-time. By proactively anticipating and testing potential failure modes early, teams can embed automatic recovery mechanisms directly into their operational workflows, significantly reducing downtime and maintaining user trust.

Moreover, viewing errors not as failures but as valuable learning opportunities enables continuous improvement and innovation. Each encountered issue provides critical insights, helping to enhance model robustness and informing future development cycles. This iterative process helps build a resilient system that not only tolerates unpredictability but actively benefits from it.

Ultimately, adopting this proactive, agile approach positions organizations to better manage complexities and capitalize on the opportunities presented by their LLM applications.

How to See What Your LLMs Are Doing in Production

Visibility and control over your LLM's behavior in production are crucial for promptly identifying and responding to issues. By systematically capturing and analyzing key performance data, teams can maintain operational integrity and continuously enhance model performance.

Effective monitoring requires multiple layers of visibility, each serving distinct but complementary roles:

By deploying and integrating these monitoring layers, teams achieve comprehensive visibility and precise control over their LLMs, fostering rapid issue detection, informed troubleshooting, and continuous improvement.

Common Production Issues

Even well-instrumented LLM systems can fail in subtle, repeatable ways. These aren’t just random bugs—they’re patterns that emerge at scale, often hiding in plain sight until user trust or costs take a hit. By recognizing these failure modes early, you can detect them faster, mitigate them more effectively, and even prevent them entirely with the right guardrails.

1. Hallucinated Facts

When the model confidently makes things up—fake statistics, nonexistent APIs, or distorted facts—it can erode user trust instantly.

• Detection: Run factuality checks, track contradiction patterns, and compare against retrieval sources.

2. Function Call Issues

Agentic systems often struggle with tool usage. Common issues include:

• Wrong function/tool call: The agent chooses the wrong tool for the task.
  Example: Searches for weather instead of movies.

• Incorrect arguments: The agent calls the right tool but with malformed inputs—missing fields, wrong types, or bad formats.
  Example: Uses genre=123 instead of genre="comedy".

• Illogical call sequence: The agent calls tools out of order.
  Example: Tries to analyze data before gathering it.

• Mismatch with user intent: Technically valid calls that don’t reflect what the user asked for.
  Example: User asks for all customers, but the agent limits to five.

• Detection: Run targeted checks for function call validity, argument correctness, and consistency with user intent.

3. Policy or Safety Violations

LLMs can produce harmful or noncompliant content—often subtly, and without clear indicators.

• Unsafe or biased outputs: Jailbreaks, offensive content, or policy violations may slip through unnoticed.

• Ambiguous inputs: Edge cases can push models into gray areas with unclear compliance implications.

• Behavioral drift: Responses that were safe yesterday may quietly become risky tomorrow.

• Escalating stakes: A single bad output can create outsized regulatory or reputational fallout.

• Detection: Use moderation APIs, jailbreak phrase filters, and automated compliance checks.

4. Conversation Loops

Sometimes the model just… doesn’t stop. It might re-ask a question, re-explain the same concept, or repeatedly retry a failed action. These loops waste tokens, frustrate users, and degrade the experience.

• Detection: Monitor for excessive turns, repeated tokens, or high similarity between conversation steps. Flag sessions where the agent revisits the same state or goal multiple times.

Control Strategies for Continuous Improvement

Even with excellent observability, knowing what's broken isn’t enough—you need a clear path to improve. Unlike traditional software, LLM applications often behave like shifting sand: outputs evolve, APIs change, and user behavior continuously generates new edge cases. That's why your strategy can't just be reactive—it must be iterative, cautious, and measurable.

Here are five strategies to help you control your model’s evolution in production:

1. Instrument from Day One

Build your observability foundation early. Traces, logs, and metrics should be wired in before the first user prompt. You can’t improve what you can’t see.

2. Prompt Iteration Loop

Your prompts are living code. Use monitoring insights to identify weak spots, make targeted tweaks, redeploy, and measure the outcome. This loop is the fastest, safest way to adapt to shifting model behavior.

3. Patch with Examples (Instead of Fine-Tuning)

When the model consistently fails in a certain pattern, collect those examples and either augment your system prompt or use RAG techniques to inject corrective context. Avoid model-level fine-tuning unless absolutely necessary—context-level fixes are faster, cheaper, and safer.

4. Canary Releases & Version Tagging

Never release blind. Use version-tagged traces to compare old vs. new behaviors, and canary deployments to roll out changes gradually. You’ll spot regressions before they impact most users.

5. Human-in-the-Loop for High-Risk Paths

Certain flows—like policy-sensitive conversations or key business decisions—should be flagged for review. This ensures safety while buying time for further automation.

Together, these strategies form a control plane for your LLM. They let you intervene with precision, improve with confidence, and avoid treating every change like a gamble.

Putting It All Together with Okareo

Once you're running an LLM in production, the last thing you want is to glue together five monitoring tools, duct-tape a few dashboards, and hope for the best. You need a control tower—something built for the unpredictable, high-velocity nature of modern AI systems.

Okareo provides a unified platform to observe, evaluate, and continuously improve your LLM-powered apps—without cobbling together your own infrastructure. It combines deep tracing, customizable monitors, automated checks, and real-time analytics into a single, production-ready interface.

Here’s what Okareo brings to your stack:

• Full-Context Traces – Capture every prompt, response, latency, function call, and metadata with a single line of instrumentation.

• Segmented Monitoring – Group traces into meaningful units (e.g. error-prone flows, slow interactions) and apply targeted monitors.

• Automated Evaluation – Score responses for factuality, policy violations, or structured output correctness using customizable LLM-as-judge checks.

• Live Dashboards & Alerts – Stay ahead of drift, spikes, or regressions with real-time metrics and threshold-based alerts.

Okareo was built for teams shipping real LLM apps in messy, dynamic environments. Instead of reacting to surprises, you’ll be able to predict, isolate, and improve them—before users ever notice.

Conclusion – Confidence Through Controlled Chaos

There’s no such thing as a perfectly predictable LLM. The moment your app goes live, it enters a world of messy prompts, edge-case inputs, shifting APIs, and real human unpredictability. That’s not a bug—it’s the nature of the system.

Controlled chaos is about accepting that truth, and building systems that thrive in it.
It’s not about eliminating every surprise. It’s about seeing them coming, understanding them deeply, and responding fast enough that they never become problems.

With traceability, continuous feedback loops, and clear control strategies, your LLM stack becomes more than just a clever prompt—it becomes a resilient, evolving system. One that gets better every week. One that users can trust. One that gives you leverage instead of stress.

Because in the world of production AI, it’s not the teams who avoid chaos that win—it’s the ones who control it.