Near-100% Accurate Data for your Agent with Comprehensive Context Engineering

Context

To be successful, agents typically need to combine multiple steps and execute business logic reflective of real-life decisions. But, as developers rush to deploy these autonomous agents, they are slamming into a wall: the compounding error problem of accuracy. To understand why agentic workflows require near-100% accuracy on questions that are answerable by your database data, let’s look at the numbers: Assume an accuracy of 90% in a single-step AI process. You ask a question; you get a correct answer 90% of the time. But in an agentic workflow, the AI takes multiple dependent steps – and errors compound exponentially. Let’s run the numbers on a 90% accurate agent: One step: 90% success rate. Two steps: 0.90 × 0.90 = 81% success rate. Five steps: 0.90^5 = 59% success rate. Now, imagine that same five-step workflow running on an 80% accurate agent. The success rate plummets to just 33%. In a business context, even 90% accuracy is often insufficient. And 59% or 33% success rate is downright catastrophic. Indeed, in many industries near-100% accuracy is needed, because the agentic application is customer-facing and inaccuracies lead to loss of trust and loss of revenue.…

For builders

To be successful, agents typically need to combine multiple steps and execute business logic reflective of real-life decisions.