Enterprises are moving at breakneck speed to adopt AI-infused applications (AIIAs) and dynamic, multi-agent workflows. Yet, a stark reality has emerged across tech stacks: organizations are deploying complex, probabilistic systems far faster than their capacity to test them.
In this Forrester report, “Trustworthy AI Starts With Testing And Evals, Not Deployment”, analysts point to a fundamental shift in software delivery. The consensus is clear: traditional testing practices alone cannot validate non-deterministic systems. Treating production monitoring as a safety net leaves businesses exposed to severe quality, security, brand, and legal liabilities.
SimSpace was cited as a company that contributed their time with an interview for the research behind this report. In your complimentary copy of the report, you’ll receive Forrester’s unified framework for testing AI-Infused applications and an explanation of how our SimSpace AI Proving Grounds methodology addresses this gap by validating, operationalizing, and building AI agents inside realistic replicas of the production environment prior to deployment.
Forrester’s Unified Framework for Testing AI-Infused Applications
Risk-Proportional Testing
Testing depth must match business impact and blast radius. Low-risk apps can rely on lightweight evals, but agentic systems with execution
authority demand deep behavioral evals, adversarial stress-testing, and strict release gates.
End-to-End Workflow Validation
Teams must evaluate the complete system harness—models, prompts, permissions, API connections, memory, and UI—rather than
judging isolated prompts.
Adversarial Red Teaming
Probing multi-turn agent interactions against indirect prompt
injection, policy evasion, data leakage, and unexpected tool calls.
Converting Production Learning into Pre-Deployment Assets
Transforming runtime
edge cases and failure traces into automated pre-release regression packs.
