Meet your new L3 Support Engineer: The Player

Many companies wouldn’t trust an AI agent to solve their hairiest customer escalations. And with good reason. Most AI agents aren’t much better than an overeager intern ready to chase down dead-end paths based on poorly formed assumptions. But what if the AI agent was more than that: what if it had the technical depth of your best architects, a flawless memory of all past customer issues, a relentless drive for functional perfection in your code? What if it was an integrated member of your team with full context, access to all the tools your best support engineers had, and knew when to ask for help when it needed it? Meet The Player: an autonomous AI agent that handles a specific issue or ticket from start to finish.

How it works

Resolving a customer issue depends on bringing together the right combination of the people, the process, and the context. For a support engineer to bring together the right expert with the right context to apply to the situation can be extremely time-consuming and disruptive for everyone involved. Most AI agents find this task nearly impossible. What makes PlayerZero stand out is its capacity to collect and simulate thorough contextual data, which connects people and processes. Here’s how we do it:

1. Define the process

First, encode your triage workflow, considering steps, actions, and locations. From start to finish: intake, triage, analyze, fix, test, document, and close. PlayerZero can work with you, alongside you, and then drive autonomy.

Build architectural context

As PlayerZero progresses through these stages, it evaluates its accessible information, concentrating on the codebase, which defines the truth of the software. By analyzing all code repositories, regressions, and fixes, it builds a powerful knowledge graph of your entire system. Not all context lives in your code, however, so you’ll also need to connect to any application that your human support engineers need access to, such as Linear, JIRA, Zendesk, ServiceNow or Confluence. Give it permission to move tickets, change columns, and post updates with an audit trail.

PlayerZero can connect to any remote MCP server, such as:

• Linear: Issues and projects

• Jira: Tickets and epics

• Azure DevOps: Work items and boards

• Salesforce: Cases and incidents

• Zendesk: Support tickets

• ServiceNow: Incident management

• Confluence: Knowledge base integration

Define the triggers and approvals

Once the process and context are in place, you decide how much autonomy you want to give The Player. At any stage, you can instruct The Player to ask for approvals at the right time to the right people. It allows you to begin with human approvals and slowly automate when you're ready. As you mature, you can use triggers that will prompt The Player to investigate an issue autonomously. Common triggers include: an issue with a specific tag, an issue assigned to a certain project, a keyword in the ticket ID or when a ticket moves to a new status.

Out-of-the-box workflow

To get you started, we have an out-of-the-box workflow that you can use and customize to match your organization:

1. Intake & Triage (Entry Stage)

This is where every Player begins its journey. The AI acts as your L3 support intake agent, responsible for:

• Extracting key facts: What's broken, when did it happen, what's the impact, and what's the environment?

• Asking targeted questions: The AI asks one question at a time (2-3 max) to gather the most directional guidance

• Classifying the issue: Bug, performance issue, data problem, user error, or feature request

• Assessing severity: Based on user impact and business criticality

• Searching for related tickets: Checking if similar symptoms or resolutions exist

• Basic troubleshooting: Attempting to resolve lower-severity issues directly

Exit paths: The Player can either promote to "RCA" (Root Cause Analysis) if engineering investigation is needed, or directly to "Document & Close" if the issue is resolved, determined to be user error, or identified as a known issue.

Move to next stage: Auto approve

2. RCA - Root Cause Analysis (Entry Stage)

When an issue requires deeper investigation, the Player transitions to this expert debugging stage. Here, the AI:

• Studies the codebase: Uses deep research to understand APIs, architectures, data models, and flows

• Generates hypotheses: Creates up to 5 ranked hypotheses with defined mechanisms and predictions

• Designs diagnostic scenarios: Creates specific, orthogonal test scenarios (skipping if the fix is obvious like a typo)

• Runs simulations: Executes scenarios and analyzes results

• Declares root cause: When one clear failure is identified

Exit paths: Promotes to "Fix" when root cause is confirmed, or to "Document & Close" if it's determined to be user error, not reproducible, or outside scope.

Move to next stage: Auto approve

3. Fix (Work Stage)

This is where code remediation happens. The AI becomes a surgical code remediation agent that:

• Understands fix scope: Reviews root cause and reads relevant code

• Researches patterns: Studies similar solutions in the codebase

• Creates a fix plan: Uses the todo tool to track files to modify and changes needed

• Implements changes: One file at a time, maintaining code quality and style consistency

• Verifies with simulations: Re-runs all failed scenarios until they pass

• Final quality check: Reviews for side effects and regressions

Exit paths: Can move to "Test" when implementation is complete, back to "RCA" if the fix doesn't work, or to "Document & Close" if the issue shouldn't be fixed.
Important note: This stage requires human approval before the Player can proceed.

Move to next stage: Requires human approval

4. Test (Work Stage)

The QA validation stage ensures fixes are production-ready. The AI:

• Studies the fix: Maps the impact radius and searches for related tickets

• Creates comprehensive test plan: Including happy path, edge cases, integration tests, regressions, and security checks

• Designs test scenarios: Specific, executable, independent tests

• Executes systematically: Tracks all test results

• Iterates on failures: Fixes code or tests, then re-runs ALL tests

• Provides final report: Documents all tests executed and confirms 100% pass rate

Exit paths: Promotes to "Document & Close" when all tests pass, or back to "Fix" if issues are discovered.

Move to next stage: Requires human approval

5. Document & Close (Terminal Stage)

The final stage creates comprehensive documentation before closing the issue:

• Summarizes the issue: Original problem, severity, impact, and timeline

• Documents root cause: What broke, why it happened, and how it was discovered

• Describes the fix: Changes made, files modified, and approach taken

• Lists verification: All tests run and validation performed

• Captures learnings: Prevention measures and similar issues to watch

• Closes the ticket: Updates status and notifies stakeholders

This is the last stage—once here, the Player's work is complete.

Human-in-the-loop intelligent approver suggestions

You might be curious how The Player knows who to ask for help and approval at each stage. To determine the best approver, PlayerZero analyzes:

For Engineering Workflows:

• Commit history: Who has worked on the affected code recently?

• Code ownership: Who is most familiar with this area?

• Related changes: Who authored similar fixes or features?

For Support Workflows:

• Ticket category expertise: Who handles similar ticket types?

• Subject matter experts: Who has resolved related issues?

• Team routing: Which support tier should review?

Bidirectional Sync: The Player as a Team Member

Because the humans on the team need visibility into what is happening with issues, The Player is designed to truly act like a member of the team. Not only does it read your ticketing systems, but it also updates systems of record just like the other support team members. For example, the agent will not only move tickets between columns on a Linear board but will also:

• Adds comments with investigation findings

• Updates priority and severity labels

• Assigns tickets to relevant team members

• Links related issues and pull requests

• Documents resolution and closes tickets

This means your existing processes continue to work—PlayerZero just speeds them up.

Conclusion: End-to-End Autonomy

Every step maintains state in your existing systems. No magic, no black boxes. Think of it as having a dedicated engineer who never sleeps, can search through your entire codebase instantly, and follows a structured workflow to investigate and resolve problems. Throw the agent at your backlog.

The future of support and engineering workflows isn't about replacing humans—it's about augmenting them with AI agents that handle the toil, maintain the audit trails, and escalate only when human judgment is required.

Ready to stop staring at your backlog? PlayerZero is ready to start working on it for you.