How to monitor an AI agent
Introduction
Monitoring your AI agent is the cornerstone of maintaining trust, ensuring performance, and delivering continuous value to your users and business.
Monitoring an AI agent is not just a technical necessity — it’s a strategic practice to maintain trust, performance, and alignment with your business objectives. Without an effective monitoring setup, issues may go unnoticed, degrading user experience and wasting resources.
1. Define the right metrics to measure success
Choosing the right metrics is the first step to objectively track your agent’s performance and impact.
Start by selecting Key Performance Indicators (KPIs) that reflect your agent’s core objectives. These KPIs become your North Star for monitoring and continuous improvement:
- 🎯 Accuracy: How often the agent produces correct or useful responses. Aim for ≥ 95%.
- ✅ Task completion rate: Percentage of user requests or workflows successfully completed. Target ≥ 90%.
- ⏱️ Latency: Monitor average response time, with alerts if thresholds are exceeded (e.g., < 500 ms, alert > 1000 ms).
- ⚠️ Error rate: Track failures or anomalies. Keeping it below 5% is a solid baseline.
- 🖥️ Resource usage: CPU, memory, and API success rates help avoid system overload or cost issues.
- 😊 User satisfaction: Collect quantitative scores (e.g., NPS) and qualitative sentiment to capture the user perspective.
Insight : KPIs must be business-relevant and actionable. For example, a chatbot’s response accuracy impacts customer satisfaction directly, while API call success influences costs and uptime.
2. Real-time visibility: the backbone of proactive monitoring
Monitoring isn’t about checking dashboards once in a while — it’s about continuous, live insight into your agent’s behavior.
- Detailed logs Capture every interaction, including inputs, outputs, errors, and timestamps. This data is invaluable for diagnostics and auditing
- Custom dashboards Tailored views focusing on the KPIs that matter most, accessible by product managers, engineers, and stakeholders
- Stream processing & anomaly detection Use streaming analytics to spot unusual patterns or spikes immediately — such as sudden drops in accuracy or spikes in errors.
Tip: Equip your team with real-time alerting systems that notify the right people before customers notice an issue.
3. Alerting and troubleshooting: fast reaction wins the day
Even with the best monitoring, incidents happen. What makes the difference is how quickly you detect and resolve them.
- Define automated alerts on critical KPIs — trigger emails, Slack messages, or incident tickets when thresholds are breached.
- Use log analysis tools to trace back the root cause of failures or degraded performance swiftly.
- Build playbooks for common issues to empower rapid resolution without guesswork.
4. Best practices for sustainable monitoring
- Automate fixes : When possible, automate responses to known issues (e.g., restart a service or switch fallback models).
- Review data regularly : Monitoring is a continuous process. Schedule periodic deep dives to analyze trends, recalibrate thresholds, and adjust strategies.
- Ensure security and compliance : Log sensitive data securely, restrict access, and audit usage to comply with internal policies and regulations.
5. Insight terrain : Nicolas Schuhl, Applied AI Engineer, Mistral AI
“GenAI is a Fast Moving Landscape. Breakthroughs like significant speed improvements, cost reductions, or new modalities such as images or voice, are coming every month.
Overloading your mind with complex tools is a way to avoid spending the amount of time on the iteration work required to build AI tools. I like the ‘just-in-time’ tooling regarding Generative AI and the ‘bare metal’ setup.
Being opinionated on your AI stack and strategy is essential to a successful proof of concept.
AI should remain a tool for addressing user pains. Avoid overthinking; stick to user pain points while focusing on iterative development. Start with a clear objective, build an MVP, gather feedback and iterate.
Moving Generative AI to production brings a suite of problems: quality of service, addressing scalability, cost, and maintainability.
You sometimes realize that the fancy prompt can’t reach production because it will be more expensive than the price of your solution, or you don’t have enough on a given model provider. You sometimes realize that the ugly prompts are staying in production because you have no clues of a small impact on your prompt on your product.
Anticipate the rabbit holes of production deployment by checking the quotas of your model provider and the quality of service, and by integrating A/B testing from the start for continuous iteration.
6. Solution to streamline your monitoring workflows : focus on Basalt
Monitoring an AI agent requires the right tools. Basalt specializes in AI feature monitoring, offering:
- Seamless integration with your existing tech stack through APIs.
- Collaborative environments where PMs, engineers, and data scientists can jointly analyze data.
- Automated reports and anomaly detection tailored to AI-specific challenges
