.jpg)
August 5, 2025
From demo to deployment: Lessons from Kairos Studio on launching AI support agents
If you're building AI agents for support, you've probably heard the hype: faster resolution times, happier customers, superhuman efficiency. But what happens after the demo?
In last week’s workshop, we sat down with Kelly Feeney and Kelly Snodgrass from Kairos Studio, which helps companies navigate the messy middle between AI experimentation and real deployment. Here’s what they’ve learned helping teams actually get agents into production.
1. You cannot automate chaos
Before you launch, your processes need to be stable and well documented.
Kairos looks for three key signals of AI readiness:
Stable processes that are well defined and not changing constantly. If your workflows are messy, AI will just magnify the problems.
Strong knowledge management with clearly structured content, keyword tagging, and regular updates. AI is only as good as the information you feed it.
Cultural readiness across the team, not just leadership. Agents succeed when frontline teams are on board. Appoint internal ambassadors who can support the rollout and act as change agents.
2. Start small, stay scoped
The best AI agents do one thing really well.
To define the right scope:
Choose a single high-volume task that is clearly defined. For example, routing requests or answering common “how-to” questions.
Use AI-specific success metrics such as “resolve 70 percent of billing questions without human handoff” rather than general goals like “improve customer satisfaction.”
Map exceptions and escalation triggers early. Knowing what AI should not do helps define the boundaries.
3. Scope creep is real
If you do not get ahead of it, small asks turn into major detours.
Here is what helps:
Create a “not doing” list for version one. This avoids confusion and keeps priorities clear.
Use phased rollouts with clear criteria for each stage before expanding.
Test on past tickets to safely evaluate performance before going live. This is a strategy recommended by 14.ai.
Keep weekly check-ins with active stakeholders. Use a simple 3Ws format: Wins (what worked), Warnings (what needs help), Watch this space (what is coming next).
4. AI will not replace your team
One of the biggest myths is that AI adoption leads to layoffs.
In reality:
Most successful deployments augment humans, freeing them from repetitive work so they can focus on more valuable tasks.
Internal AI agents are just as impactful as customer-facing ones. These copilots help teams surface answers faster and work more efficiently.
Some roles will evolve, so investing in upskilling and change management is key.
Also, training your team is not optional. This is not a one-time onboarding session. Plan for a multi-month feedback loop to help both the team and the agent improve.
5. Prioritize by volume and complexity
Not everything should be automated right away.
The Kairos team recommends mapping tasks by:
Volume: how often the task occurs
Complexity: how much expertise it requires
Start with high-volume, low-complexity tasks. These usually deliver the fastest wins. Also, talk to the people doing the work. If they make the same decision ten times a day, it might be a great candidate for automation.
6. Treat pilots like product launches
Too many pilots fail because they lack structure.
Here is what helps:
Define success metrics at the start, and share them widely
Celebrate small, high-confidence wins. Even a 3 percent impact can be a good start if it is consistent and accurate
Budget for full deployment. The pilot might cost $10,000, but rollout can be 3 to 5 times more with integration, training, and ongoing maintenance
Test internally first to minimize risk and gather feedback before expanding
7. Do not overbuild too early
It is easy to waste time and money chasing the perfect stack.
Start lean:
Use existing tools like your current CRM or helpdesk with AI plugins
Monitor feedback loops from the beginning. Real-time tracking of what is working and what is not helps you iterate quickly
Avoid custom models too soon. Most teams do not need to train their own AI. Off-the-shelf tools can go a long way when paired with clear criteria and good design
Many thanks to the Kairos team for the thoughtful advice. If you're gearing up for a launch of your own, we hope this gives you a clearer path forward.
