Agent Insight

Aissist Agent Insight is an AI agent evaluation and quality assurance platform designed to help businesses measure, compare, and continuously improve the performance of both AI and human agents. Instead of treating AI performance as a black box, Agent Insight provides a transparent evaluation framework in which every interaction can be measured against standards and metrics defined by the business itself. This allows organizations to understand how well their AI agents are performing, how their results compare with human teams, whether they follow established quality guidelines, and where improvements are needed.

A central feature of Agent Insight is its unified evaluation system for AI and human agents. Both types of agents can be assessed using the same standards, metrics, and evaluation framework, making performance comparisons more consistent. Businesses can examine factors such as resolution quality, first-contact resolution, customer satisfaction, accuracy, QA compliance, policy and disclosure accuracy, tone, escalation discipline, and other custom metrics. Results can also be segmented by case type, communication channel, and language, helping organizations identify where AI or human agents perform differently across specific operational scenarios.

Agent Insight is designed around business-defined quality standards rather than a fixed vendor scorecard. Companies can use their existing customer service or call center QA guidelines as the basis for evaluating AI interactions. This allows organizations in different industries to define what quality means for their particular operation. For example, a financial services company may prioritize compliance and disclosure accuracy, while an ecommerce business may focus on refund-policy adherence and resolution rates. Custom evaluation criteria can be applied consistently across every interaction.

The platform also provides detailed visibility into individual agents and sessions. Managers can open a specific AI or human agent to review performance trends, daily metric breakdowns, and comments associated with individual scores. At the session level, Agent Insight provides a detailed evaluation showing each metric, its score and weighting, the reasoning behind the evaluation, and timestamped examples from the conversation. This evidence-based approach allows teams to understand why a particular score was given instead of relying only on an overall number. Each evaluation can highlight what went well, identify opportunities for improvement, and provide concrete recommendations.

Another major component of Agent Insight is its connection between evaluation and continuous AI improvement. Evaluation results are converted into structured signals that identify performance gaps, quality issues, and potential root causes. These signals feed into Aissist's Evolve continuous optimization layer, which can use the findings to improve AI guidance, knowledge, and escalation rules. The following evaluation cycle then checks whether the changes produced better results. This creates a continuous loop of measuring performance, identifying weaknesses, applying improvements, and verifying the outcome.

Agent Insight is part of Aissist's broader AI Operational Layer and Pulse platform and is designed for organizations operating AI-powered customer service and business workflows. It integrates with platforms such as Intercom, Zendesk, Freshdesk, Kustomer, Front, Gorgias, Salesforce, and HubSpot. The platform is particularly relevant for businesses that need greater visibility into AI-assisted customer operations, quality assurance, compliance, and the transition between human and automated service.

Overall, Aissist Agent Insight combines AI evaluation, human-agent benchmarking, customizable QA standards, detailed interaction analysis, and continuous optimization into a single system. By evaluating interactions against business-defined criteria and turning evaluation results into improvement signals, the platform provides organizations with a structured way to monitor AI quality, identify operational gaps, and continuously improve the behavior of their AI agents.