Update
Oct 19, 2025
Giga Team
Contents
Every AI agent handles thousands of conversations a day.
Each ticket captures the customer’s intent, actions taken by the agent, and how the exchange ended: resolved, transferred, or abandoned.
Individually, these records are simple to review. At scale, they become complex. The more data you collect, the harder it becomes to understand why certain interactions work while others repeatedly fail.
Insights reason through that complexity.
It detects recurring operational patterns and translates them into clear explanations paired with actionable steps.
Each insight explains what behavior is emerging, which policy or knowledge element influences it, and how adjusting that element is expected to improve key performance metrics such as resolution rate, transfer reduction, customer satisfaction, or even revenue.
From observation to reasoning
Traditional analytics report numbers: total calls, average durations, and transfer counts, but stop short of explaining why they happen.
Insights takes a different approach. It analyzes every ticket, both voice and text, and applies structured reasoning to infer cause and effect between agent behavior and business outcomes.
For example, it might detect that a specific intent consistently leads to transfers after a certain response pattern. Instead of comparing to other resolved cases, the system identifies the missing or inconsistent step that prevents resolution, such as a missing confirmation, an unclear instruction, or an outdated rule.
It then produces a written explanation, a precise set of recommended actions, and a projected quantitative impact if those actions are implemented.
These explanations are data-grounded hypotheses that can be reviewed, validated, and acted upon by operations and engineering teams within minutes.
Example: multilingual resolution gaps
Consider a voice agent that supports several languages.
Insights might find that Spanish and French delivery calls escalate to a human far more often than English calls, even when the issue type is identical.
By reviewing those tickets, the system recognizes that the English agent consistently uses a clear confirmation phrase before proceeding, while the other languages skip it or use inconsistent wording.
The generated insight could read:
Calls in Spanish and French show higher transfer rates due to inconsistent confirmation phrasing.
Standardizing this confirmation step across languages is projected to reduce transfers by around ten percent.
Each explanation connects directly to the relevant configuration in Agent Canvas, the environment where Giga’s agents are authored and managed. Teams can review the underlying rule or content, view related tickets firsthand to understand the context, implement the change, and later measure its outcome within the same ecosystem of data.
How findings are organized
Insights classify each finding by its underlying mechanism.
Policy modifications describe changes to how the agent behaves, including its logic, permissions, or flow design.
Knowledge gaps point to missing or inconsistent information that prevents the agent from handling a situation autonomously.
Findings are automatically ranked by their estimated impact on the chosen metric, ensuring that teams focus first on the changes most likely to deliver meaningful operational improvement.
Continuous reasoning in practice
Once running, Insights continuously refreshes its understanding of the system.
New conversation data enters the pipeline, hypotheses are re-evaluated, and recommendations are updated based on the latest evidence.
Teams review the proposed explanations, decide which ones to act on, and the system observes the results of those actions in the next data cycle.
This loop allows both the agent and the organization around it to learn from their own operations without relying on ad-hoc analyses or manual log reviews.
Each cycle strengthens the connection between observation, decision, and measurable effect.
Starting points for new agents
To simplify onboarding, new agents automatically include a baseline set of insights that help teams analyze performance from day one.
These initial analyses cover common dimensions such as abandonment, resolution, and sentiment, identifying where users tend to drop off, where human escalation occurs, and where negative feedback clusters across intents or languages.
They provide an immediate, interpretable foundation that grows richer as more data accumulates.
Why it matters
As conversational systems scale, understanding their behavior becomes an engineering challenge as much as an operational one.
Insights brings structure to that understanding.
It turns raw conversation data into reasoning-based explanations tied to evidence and accompanied by clear next steps.
This framework enables teams to evolve their agents deliberately, grounded in data and causality.
