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.
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:
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.
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.
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.
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.
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.
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.
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
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
A 75% drop in manual work crafting and refining AI instructions, freeing the team to focus on more complex problems.
A 70% leap in agent accuracy, meaning tasks are completed correctly the first time, vastly improving user trust.
85% stability across all integrated tools, ensuring a smooth and predictable experience no matter where an agent is working.
Accelerated revenue realization by 30% by launching dependable AI-driven workflows sooner.
Increased task completion rates by 25%, directly improving user confidence and adoption of agentic AI.
Minimized operational and compliance exposure by surfacing AI failure scenarios before enterprise rollout.
Reduced post-production support costs by 35% through stable, production-ready AI agent integrations
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.
Turned Rigorous Testing into Reliable Automation
The rigorous testing framework transformed how the platform operates. By validating AI behavior at scale, the team eliminated critical vulnerabilities and built confidence in the system's reliability. The result: a platform that users can trust, teams can iterate faster, and the business can scale without compromise.
About Indium
Indium is an Al-driven digital engineering company that helps enterprises build, scale, and innovate with cutting-edge technology. We specialize in custom solutions, ensuring every engagement is tailored to business needs with a relentless customer-first approach. Our expertise spans Generative Al, Product Engineering, Intelligent Automation, Data & Al, Quality Engineering, and Gaming, delivering high-impact solutions that drive real business impact.
With 5,000+ associates globally, we partner with Fortune 500, Global 2000, and leading technology firms across Financial Services, Healthcare, Manufacturing, Retail, and Technology-driving impact in North America, India, the UK, Singapore, Australia, and Japan to keep businesses ahead in an Al-first world.
