How We Built an AI Agent That Solves Support Tickets in Minutes

We Got Tired of Spending Hours on Every Support Ticket

This is Part 1 of our 10-part "AI Adoption Journey" series, where we share what we have genuinely built, deployed, and learned about AI agents in our own operations.

Every software company knows the pain. A support ticket arrives: "The report isn't showing data for the last three days." Simple enough on the surface. But to investigate it, someone needs to check the application logs, query the database, review recent code changes, verify API endpoints, check scheduled jobs, and cross-reference configuration files. Two hours later, you've found the root cause -- a cron job silently failed after a dependency update.

We lived this reality running our own SaaS products. According to industry data, the average IT incident takes 4-6 hours to resolve through manual investigation, and Gartner estimates enterprise downtime costs USD $5,600 per minute. For a small Australian software company, that translates to burning through your most expensive resource -- senior developer time -- on detective work instead of building product.

So we built something about it. Not a chatbot. Not a ticketing system with better routing. An actual AI agent that investigates support tickets the way a senior engineer would -- except it does it in minutes instead of hours.

The Core Problem: According to Master of Code (2026), 51% of organisations now have AI agents running in production, yet most are limited to classification and routing. Genuine investigation -- the kind that requires reading logs, querying databases, and connecting evidence -- remains overwhelmingly manual.

What We Actually Built (And Why It Matters)

We deployed an internal AI agent we call the "Solve8 BMS Agent" for our own SaaS support operations. It lives inside Microsoft Teams -- the tool our team already uses every day -- and it is powered by Claude AI as the reasoning backbone.

Here is what happens when a support ticket comes in:

The agent does not just classify the ticket or suggest a knowledge base article. It actively investigates. When someone reports "data not loading," the agent:

1. Searches application logs for errors, warnings, and anomalies in the relevant timeframe
2. Queries the database to verify data integrity, check recent changes, and identify gaps
3. Reviews code changes to find commits that may have introduced the issue
4. Cross-references configuration, scheduled tasks, and external dependencies
5. Produces a structured investigation report with evidence, probable root cause, and recommended resolution steps
6. Stores the investigation for future learning -- so similar issues get resolved faster next time

The critical design decision: human-in-the-loop. The agent recommends actions, but a human engineer reviews and approves before any fix is applied. This is not optional for us -- it is a foundational architectural principle. According to KPMG's 2026 AI Pulse report, 75% of enterprise leaders cite security, compliance, and auditability as the most critical requirements for AI agent deployment.

Why Microsoft Teams Integration Changed Everything

One of the biggest lessons we learned early: if the AI does not live where your team already works, nobody uses it.

We deliberately built the agent as a Microsoft Teams bot rather than a standalone dashboard or separate application. The reasoning is straightforward:

• Zero context switching -- engineers ask questions and receive investigation reports in the same interface they use for all communication
• Natural language interface -- no query syntax, no special commands, just describe the issue in plain English
• Conversational follow-up -- ask the agent to dig deeper into a specific area, check a different timeframe, or expand on a finding
• Shared visibility -- investigation reports are visible to the team, building collective knowledge
• Mobile access -- Teams runs on phones, so on-call engineers get investigation results wherever they are

This aligns with the broader industry shift. Gartner forecasts that 40% of enterprise applications will embed task-specific AI agents by 2026, up from less than 5% in 2025. The pattern is clear: AI that integrates into existing workflows wins over AI that requires new workflows.

The Results: Before and After

We have been running this agent in production for our own support operations. Here is what the numbers look like.

The time reduction is the headline number, but it is not the most valuable outcome. The real gains are:

Consistency. A senior engineer having a good day might investigate thoroughly. A junior engineer on a Friday afternoon might miss things. The AI agent follows the same rigorous process every single time.

Documentation. Every investigation produces a structured report with evidence. No more "I fixed it but I don't remember what I did." This matters enormously for compliance and knowledge transfer.

Learning loop. The agent stores investigation history. When a similar issue appears, it draws on prior investigations to resolve faster. This compounds over time -- the agent gets more valuable the longer it runs.

The ROI Case for AI Investigation Agents

Let us put real numbers on this. These calculations are based on our own experience combined with industry benchmarks.

These numbers scale with team size and ticket volume. For a mid-market software company processing 30-50 tickets per week, the savings multiply accordingly. Industry data from Master of Code (2026) indicates companies report average returns of 171% on AI agent investments, with 74% achieving ROI within the first year.

Five Lessons We Learned Building This Agent

Building an AI agent that actually works in production taught us things that no vendor whitepaper will tell you. Here are the five most important lessons.

1. Start With Investigation, Not Resolution

Our first instinct was to build an agent that fixes issues automatically. That was wrong. The investigation step -- understanding what happened and why -- is where 80% of the time goes. Resolution is usually straightforward once you know the root cause.

By focusing on investigation first, we built trust with the engineering team. They could see the agent's reasoning, verify its evidence, and learn from its approach. Jumping straight to automated resolution would have created anxiety and resistance.

2. The Conversational Interface Is Non-Negotiable

We experimented with a form-based interface initially. Fill in the ticket number, select the system, choose a severity. Nobody used it. The moment we switched to a natural language conversational interface in Teams, adoption went from grudging to enthusiastic.

Engineers want to type "Why is the reporting dashboard showing stale data since Tuesday?" not fill in a form. The AI should meet humans where they communicate naturally.

3. Human-in-the-Loop Is a Feature, Not a Limitation

Some vendors position full automation as the goal. We disagree strongly. For support investigations, the human review step:

• Catches the 15% where the AI's recommendation needs adjustment
• Builds engineer understanding of the codebase and systems
• Maintains accountability -- a human signs off on every change
• Enables learning -- when engineers correct the agent, it improves

As multi-agent AI systems become more capable, the human-in-the-loop principle becomes more important, not less.

4. Investigation History Is Your Competitive Moat

Every investigation the agent performs gets stored with full context: the symptoms, the evidence gathered, the root cause found, and the resolution applied. After a few months of operation, this becomes an extraordinarily valuable knowledge base.

New team members can search past investigations. The agent itself draws on this history to resolve recurring issues faster. And when you need to demonstrate compliance or audit your support processes, every step is documented.

5. Scope It Tightly at First

We did not try to build an agent that handles everything. We started with one specific class of support tickets -- data integrity issues in our reporting system. Once that worked reliably, we expanded to API errors, then authentication issues, then performance problems.

This incremental approach is consistent with what we have seen across enterprise projects. The build vs buy decision should always start with a narrow, high-value use case before expanding scope.

Is This Approach Right for Your Business?

Not every organisation needs a custom AI investigation agent. Here is a decision framework.

The sweet spot is organisations with complex technical products, meaningful ticket volume, and the need for consistent, documented investigation processes.

How to Deploy Your Own AI Investigation Agent

If you are ready to build something similar, here is the implementation roadmap based on our experience.

Key Implementation Considerations

Choose your AI backbone carefully. We use Claude AI for its strong reasoning capabilities and ability to maintain context across complex, multi-step investigations. The model needs to handle long context windows (investigating an issue might require reading hundreds of log lines) and produce structured, evidence-based outputs.

Connect to real data sources. The agent is only as good as the systems it can access. At minimum, it needs read access to your logging infrastructure, database (read-only queries), and version control system. Each additional data source makes the agent more capable.

Design the output format. A wall of text is not useful. We structured our investigation reports with clear sections: Summary, Evidence Found, Probable Root Cause, Recommended Resolution, and Confidence Level. This consistency makes it easy for engineers to review quickly.

Plan for the learning loop. Store every investigation. Tag outcomes (correct, partially correct, needed adjustment). Use this data to improve the agent's investigation patterns over time.

The AI Adoption Journey — Full Series

This AI investigation agent was not an isolated project. It was the first step in a broader strategy of deploying AI agents across our business operations. Follow the full 10-part series:

The predictions for AI in Australian business in 2026 are not theoretical anymore. Organisations that deploy AI agents for genuine operational work -- not just chatbots and content generation -- are pulling ahead.

Try It Yourself: SupportAgent

Based on what we learned building our internal investigation agent, we built SupportAgent -- a self-hosted AI investigation tool designed for software teams.

SupportAgent brings the same investigation approach to your environment:

• Self-hosted Docker deployment -- your data never leaves your infrastructure
• Connects to Jira, Redmine, Git, SQL, MongoDB and more
• Supports .NET, Java, PHP, Angular codebases for code-level investigation
• Produces structured investigation reports with evidence and recommendations
• $69/month -- a fraction of the senior engineer time it saves

If your team spends hours investigating support tickets and you want to see what an AI agent can do, explore SupportAgent or book a 30-minute walkthrough to see it in action.

Related Reading:

• Multi-Agent AI Systems: When One AI Isn't Enough - How multiple AI agents work together for complex tasks, and why single-agent approaches hit a ceiling
• The $47,000 Question: When Custom AI Beats Off-the-Shelf Tools - A practical framework for deciding whether to build a custom AI solution or buy an existing one
• The Future of AI in Australian Business: 2026 Predictions - Where AI adoption is heading for Australian SMBs this year and beyond
• Your IT Team Isn't the Problem. Your AI Strategy Is. - Why most AI projects fail and what to do differently

Sources: Research synthesised from Master of Code AI Agent Statistics (2026), Gartner enterprise downtime estimates (2025), KPMG AI Pulse Q4 2025 report, Rootly incident response research (2025), and Salesmate AI agent adoption data (2026).