5 Real Customer Uses That Show the Benefits of AI Agent Monitoring
It’s clear that AI agent monitoring and supervision is becoming a requirement once agents move into production — this is not a nice-to-have. Teams need to know what their agents are doing, where the agents are drifting and failing, how users are responding, and what to improve next.
These five in-production customer examples that include Wayfound as the agent monitoring solution show how this important layer of supervision helps teams reduce manual work, improve outcomes, strengthen governance, and scale with confidence.
1) 531 Social uses agent monitoring to protect customer experience and accelerate growth
531 Social, a fast-growing LinkedIn social selling agentic solution, launched with strong adoption and quickly reached nearly $60K ARR in a matter of weeks. But rapid growth created a new challenge: they lacked clear visibility into how their AI agent was performing across a wide range of real user interactions. Manual log review wasn’t fast enough to spot patterns early, identify frustration to inform engineering and product design priorities, or to surface churn and upsell signals.
Rather than spending precious time trying to build some minimally viable monitoring solution, the engineering lead brought in Wayfound as the real-time agent monitoring layer for the business. Wayfound was also built for non-technical users to be able to consume and act on the information, which allowed 531 Social’s founder/CEO Darren McKee to better understand his users’ experience, protect his brand, capitalize on positive interactions, and escalate tech issues. Wayfound has become part of 531 Social’s daily operating rhythm, with Darren reviewing insights multiple times per day and intervening quickly when needed.
As Darren put it: “Our users were engaging with the 531 Social AI agent constantly, but we didn’t have good visibility into how well that agent was performing. Was it meeting expectations? Where was it delighting or sliding? Was it creating any issues with the brand? These are questions we didn’t have easy answers to in an automated way.”
2) Sauce Labs uses agent monitoring to improve support performance and speed deployment
Sauce Labs needed a better way to manage rising support demand while improving the performance of its AI support agent. Their previous chatbot setup could return documentation links, but it wasn’t meaningfully increasing ticket deflection or giving the team enough insight into what users were asking and where knowledge gaps existed. They needed richer visibility into agent behavior, user experience, and optimization opportunities.
After building a new internal LLM chatbot, Sauce Labs added Wayfound for agent monitoring and performance management. Wayfound helped the team quickly understand trends through auto-tagging and sentiment analysis, gave support leaders customizable views, and surfaced suggestions to improve the agent. The integration was lightweight, and the team began seeing value quickly.
The results were meaningful: in the first month, Sauce Labs saw a 38.5% engagement rate (up from 20% with the prior solution) and reduced agent knowledge gaps by 31%. Wayfound also helped the team create a stronger foundation for future AI agents across the business.
A strong take-away from their team: “We can send large amounts of information and get a lot of value out of Wayfound [for agent monitoring] really quickly and in a highly human-readable format,” said Afshin Mobramaein, Principal Scientist at Sauce Labs.
3) Casper Studios + Ardmore Road show how agent monitoring improves trust, adoption, and ROI in regulated financial services
During a session in 2025 Dreamforce’s Agentforce track, Casper Studios shared how financial services firm Ardmore Road used agent monitoring from Wayfound to turn AI adoption into measurable ROI in just 90 days. In a regulated industry, the challenge wasn’t only building agentic workflows, it was ensuring consistency, explainability, and confidence in production use.
Casper Studios built a modular multi-agent workflow to reduce equity review time from roughly 45–60 minutes down to about 10–15 minutes. But early adoption among Ardmore’s team lagged, because small output inconsistencies (including formatting issues) undermined analyst trust. Wayfound helped by monitoring guideline adherence, alerting the team to inconsistencies, and making it easier for analysts to inspect the agent’s reasoning and make improvements. That supervision layer helped restore confidence and accelerate adoption.
With Wayfound supervising interactions, the team reduced manual log checking and achieved major gains: adoption increased from 20% to 90%, while saving 15 hours per analyst per week.
4) 8am uses agent monitoring to replace manual trace review and bolster roadmap planning
8am, a platform serving legal, accounting, and other client-focused businesses, expanded GenAI agents across customer-facing and internal support use cases. As those deployments grew, the team faced a familiar production challenge: too much manual review and not enough visibility into how agents were performing.
Before adopting Wayfound, 8am’s principal GenAI product manager Josh Carter said he and another engineer were spending several hours every day manually reading traces in Datadog, without the real-time metrics or aggregation needed to evaluate agent behavior at scale.
Wayfound gave 8am an agent monitoring layer that helped the team move beyond manual trace review toward measurable performance management. With clearer visibility into issues, guardrail adherence, and usage themes, the team could monitor agents more efficiently and use actual interaction data to inform roadmap decisions. The impact was significant: time spent reviewing issues dropped from roughly 20 hours per week to as little as one hour per week, while confidence in deployment and governance improved.
Josh captured the shift clearly: “Now I'm spending an hour a week at most reviewing issues…. But more than that, I actually now have the data to build out a better roadmap that's going to provide more value to our customers based on what they're asking us.”
5) Startup studio 24 and Up favors use of Wayfound agent monitoring at its agentic-focused portfolio companies
Raleigh, NC-based 24 and Up is a startup studio that has co-created a number of companies that have strong usage of AI agents in their offerings, and for some, agents take center stage. [In full disclosure, Wayfound is one of the studio’s portfolio companies.]
As new companies have launched with embedded agent functionality, the studio has ensured Wayfound is part of the agent monitoring and supervisor layer to provide independent, unbiased management of agent performance. For these young startups that are in the angel investment to pre-seed funding stages, human capacity is a scarce resource – and extra time spent reading transcripts, traces, and logs is not time spent on crucially high-value work such as product development and early customer acquisition to nail their product-market fit.
Wayfound has provided four of the studio’s companies with that piece-of-mind for AI agent supervision and monitoring, substantially reducing engineering burden and allowing for non-engineering founders to more easily consume insights and recommendations to improve the agent experience in their products.
As 24 and Up studio founder and CEO, Eric Boduch said: “Agent behavior and how well it’s performing for the business should be easy to understand and fix, and the earlier this capability is introduced to a new agent, the better the experience and ROI, which are crucial for gaining market momentum and growth.”
Why these examples matter for teams evaluating agent monitoring
Across all five customer usage stories, the pattern is consistent: agent monitoring helps teams move from reactive troubleshooting to proactive improvement. It reduces manual review, improves trust and adoption, strengthens governance, and gives teams the data they need to scale AI agents with confidence.
If your team is deploying AI agents in production, the question is less whether monitoring is needed — and more how quickly you can put the right supervision system in place to position yourself best for agent success and high value return.
Sources and Additional Reading
Read more on the hidden costs of DIY / building your own agent monitoring.
View the recorded webinar featuring 8am and their agent monitoring story.
Read the full case study on Sauce Labs agent monitoring to help them scale customer support.
Read the 531 Social case study on adding Wayfound agent monitoring shortly after launch.