Kai Resolve by Gleap

Product Introduction

1. Definition: Kai Resolve is an AI-powered second-level (tier 2) support automation and root cause analysis (RCA) agent. It is a core component of the Gleap customer feedback and support platform, designed to autonomously investigate complex technical support tickets.
2. Core Value Proposition: It exists to automate the investigation of difficult support tickets that first-level AI or human agents cannot resolve, reducing engineering toil and accelerating issue resolution. By analyzing screen recordings, application logs, user session data, and source code, it provides a data-driven diagnosis, directly answers customers, or escalates with clear evidence.

Main Features

1. Multi-Source Evidence Analysis: Kai Resolve aggregates and cross-references data from multiple sources to form a diagnosis. It automatically parses console logs, network request logs, device/SDK versions, user click trails, and screen recordings captured by the Gleap SDK. It can also integrate with external data sources via the Model Context Protocol (MCP) for read-only database queries.
2. Read-Only Code Investigation: The agent can clone specified Git repositories into a secure sandbox environment to perform static and dynamic code analysis. It traces error reports through the actual source code, identifying potential bugs and citing specific files and line numbers as evidence. It operates with strict read-only permissions, ensuring no code is altered during investigation.
3. Automated Ticket Triage & Classification: When enabled, Kai Resolve investigates new bug reports before they reach a human inbox. It classifies each ticket as "answerable," "confirmed bug," "suspected bug," "feature request," "needs more info," or "inconclusive," attaching relevant evidence and confidence levels. This provides immediate, pre-sorted context for support and engineering teams.
4. Seamless Handoff to Engineering: For confirmed bugs, Kai Resolve generates a detailed coding task within the ticket. This task, complete with all investigation evidence, can be handed off to Gleap's Kai Code agent to create a fix, or to external tools like Claude Code or Cursor. This creates a direct, auditable pipeline from customer report to pull request.
5. Context-Aware Escalation: For issues requiring human judgment, Kai Resolve escalates to the team not with a blank slate, but with a structured diagnosis card. It presents options (e.g., "Is this a bug? Yes/No") based on its findings, allowing engineers to make a fast, informed decision without re-investigating from scratch.

Problems Solved

1. Pain Point: Engineering teams are frequently interrupted to diagnose vague customer bug reports, leading to context-switching overhead and slow response times. Traditional support handoffs lack technical evidence, forcing engineers to manually reproduce issues and hunt through logs.
2. Target Audience: The primary users are software development and DevOps teams at SaaS and digital product companies. Key personas include Support Engineers needing technical escalation, Engineering Managers drowning in bug triage, and Software Developers who spend excessive time diagnosing support tickets.
3. Use Cases: It is essential for: Automatically diagnosing frontend JavaScript errors reported via screen recording; identifying backend API failures by correlating user session logs with code paths; triaging a high volume of in-app bug reports to surface only genuine, high-priority issues for engineers; and providing immediate, accurate technical answers to customers on configuration or expected behavior issues.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):

   • Pros: Dramatically reduces mean time to resolution (MTTR) for technical tickets by automating evidence gathering. Provides objective, code-level diagnoses that reduce blame-shifting between support and engineering. Creates a clear audit trail from user report to code change. Integrates evidence collection (via Gleap SDK) directly into the investigation workflow.
   • Cons: Effectiveness is dependent on the quality and granularity of integrated data (logs, recordings). Requires granting read-only access to code repositories, which may be a security/compliance hurdle for some organizations. The per-investigation cost model, while pay-for-use, can become variable compared to flat-rate alternatives.

2. Key Alternatives & Differentiation:

   • Traditional Help Desks (Zendesk, Intercom): These platforms facilitate ticket management and basic automation but lack deep technical investigation capabilities. Kai Resolve differentiates by being an investigation engine that reads code and logs, whereas these are primarily communication and workflow tools.
   • AI Support Agents (Intercom Fin, Zendesk Advanced AI): These agents excel at first-level support using knowledge bases. Kai Resolve is positioned as a second-level specialist that takes over when these agents fail, focusing on technical diagnosis rather than conversational Q&A.
   • Application Performance Monitoring (APM) Tools (Sentry, Datadog): These tools excel at error tracking and performance monitoring from an infrastructure perspective. Kai Resolve differentiates by starting from the customer's narrative and UI experience (the ticket and recording) and tracing it back through logs to code, bridging the gap between user-reported symptoms and system-level errors.

Frequently Asked Questions (FAQ)

1. How does Kai Resolve's pricing work? Kai Resolve uses a consumption-based pricing model, billed per investigation at an average of $0.49 each. It is included in Gleap's Team plan ($149/month annually) and above, with investigations drawing from a separate usage credit, ensuring you only pay for the actual diagnostic work performed.
2. Is Kai Resolve secure for investigating code? Yes, it employs a security-first model. Code access is strictly read-only; repositories are cloned into ephemeral, sandboxed environments. It cannot commit, push, or execute destructive operations. Data source access via MCP is also configurable and read-only.
3. Can Kai Resolve work with tickets from email or Slack? Yes, it can investigate tickets originating from any channel integrated with Gleap, including email, Slack, and other help desk integrations. However, tickets submitted via the Gleap SDK in-app widget contain the richest automatic evidence (logs, recordings).
4. What happens if Kai Resolve cannot determine the cause of an issue? It will state the investigation is "inconclusive," which it treats as "unknown" rather than "no issue." It may ask the customer one follow-up question for missing details or, if configured, escalate directly to the human team with a summary of what was checked and what remains unclear.
5. How does Kai Resolve compare to using a general AI like ChatGPT for support? Unlike a general-purpose LLM, Kai Resolve is a structured agent with defined tools and workflows. It automatically gathers evidence (logs, code) without manual prompting, operates within a secure boundary, and integrates its findings directly into the team's ticketing and development workflow (e.g., creating coding tasks), rather than providing a standalone text response.