How Testing an Agentic AI Platform Delivered 70% Higher Agent Accuracy for a US Tech Giant
Client Overview
A leading technology company that helps businesses create intelligent agents tailored to their specific workflows. The platform allows teams to automate complex, repetitive tasks with just a few clicks, enabling them to focus on strategic initiatives. By connecting seamlessly with a broad range of business applications, the platform ensures these agents can access and act on information across the organization, minimizing manual effort and improving efficiency. As the platform scaled, maintaining its reliability and safety for end users became a growing challenge.
Claims Moved Faster Than the Process Supporting Them
The claims process involved multiple handoffs, and without a connected workflow, the client spent more time following up on claim movement and processing status.
01
Inspection Delays
Dispatch teams waited on inspection updates before claims could move forward.
02
Status Follow-Ups
Claims staff spent hours following up across teams for status updates.
03
Limited Progress Visibility
Insured customers struggled to track claim progress clearly.
04
Repetitive Coordination
Operational teams handled repetitive coordination work throughout the day.
05
Approval Delays
Dispatch approvals slowed down when updates were delayed between teams.
06
Disconnected Workflows
Claims operations lacked a connected process to manage dispatch and inspections.
Customer Updates Depended on Back-and-Forth Coordination
The delays did not stay limited to one stage of the claims process. They added pressure throughout inspections, and customer communication activities.
Coordination Overload
Claims teams regularly reviewed updates and managed documentation workflows to keep cases progressing. A large portion of operational time went into coordination activities rather than actual resolution.
Inspection Delays
Inspection updates moved across multiple teams before claims could proceed to the next stage. Delays between dispatch coordination and inspection updates slowed claim processing timelines across the value chain.
Inconsistent Communication
Insured customers often depended on operational teams for claim status updates. Without a connected process, maintaining consistent communication across claim stages became increasingly difficult.
Operational Pressure
The existing claims process created inconsistencies between workflows. They found it harder to maintain processing speed between inspections and claim handling teams.
What Stands in the Way of Reliable Agent Execution
Our clients’ AI platform was powerful, but ensuring it was reliable and safe for customers was proving to be a massive undertaking. They were grappling with a few fundamental issues:
01
The prompt problem
Writing and refining the instructions that guide their AI agents was a painstaking, trial-and-error process. It was like trying to write a perfect recipe without being able to taste the food until the very end.
02
Preparing for the worst
They knew users would push the boundaries, so they had to test their safety systems against malicious, ambiguous, and hard-to-predict edge cases. Manually dreaming up these "worst-case scenario" questions was slow and inevitably left gaps.
03
Defining clear goals
It was surprisingly difficult to build an agent that could handle a complex, multi-step request without getting lost or going off-track. Giving an AI model a simple command is easy; teaching it to navigate a nuanced task is hard.
04
Managing a web of connections
Their platform integrated with many other business tools, and ensuring the AI agents performed consistently across all of them created a tangled web of potential failures to diagnose.
A Rigorous Regimen for a Reliable AI
The team moved beyond manual testing by building a robust system to stress-test the platform at every level. Our approach was not to treat AI agents as a magic box, but as a complex system, one that needed to be continuously trained, measured, and validated for consistent performance.
And here’s how we did it.
01
Our team developed a specialized AI agent whose only job was to write and refine the prompts used for testing. This automated the most tedious part of the process, allowing us to generate over 1,400 sophisticated test scenarios to challenge the system in every way imaginable.
02
We rigorously tested more than 35 separate safety filters, checking for critical behaviors like factual accuracy, biased responses, and appropriate refusals. This intense evaluation uncovered 407 specific weaknesses, giving their engineers a precise roadmap to fortify the platform's guardrails.
03
Created and put over 50 distinct AI agents through their paces, each designed to excel at a specific skill such as complex reasoning, using tools correctly, or handling errors gracefully. This ensured that the core agent architecture was fundamentally sound and reliable
04
Finally, we tested these agents across the full suite of ten integrated business tools. This end-to-end check guaranteed that an agent could start a task in one application, pull data from another, and finish seamlessly in a third, delivering a consistent and stable user experience.
A Platform Optimized for Predictable Performance
The results of this rigorous testing translated into direct, tangible gains for the platform. The client can now build their platform faster, and their users can trust the results. The numbers tell a clear story:
Claims dispatch activities moved through an automated workflow and reduced dependency on manual coordination and repetitive operational follow-ups.
01
A 75% drop in manual work crafting and refining AI instructions, freeing the team to focus on more complex problems.
02
A 70% leap in agent accuracy, meaning tasks are completed correctly the first time, vastly improving user trust.
03
85% stability across all integrated tools, ensuring a smooth and predictable experience no matter where an agent is working.
04
Accelerated revenue realization by 30% by launching dependable AI-driven workflows sooner.
05
Increased task completion rates by 25%, directly improving user confidence and adoption of agentic AI.
06
Minimized operational and compliance exposure by surfacing AI failure scenarios before enterprise rollout.
07
Reduced post-production support costs by 35% through stable, production-ready AI agent integrations
08
Enabled faster AI rollout by validating agent behavior before production and reducing go-live delays.
These outcomes reflect a clear shift: the platform now supports smarter, faster, and more dependable automation, helping the client deliver real value to their users.