Introducing Decagon’s experimentation & A/B testing suite
June 25, 2025
Written by Jesse Zhang
Today, we’re excited to announce our experimentation and A/B testing suite that helps teams iterate on AI agents faster and deliver fundamentally better customer experiences. Coming on the heels of our series C announcement, this launch reflects our continued investment in capabilities that empower enterprises to optimize their agents at scale.
At Decagon, we believe every AI agent is its own product, tailored to your brand and business logic. Powered by Agent Operating Procedures (AOPs), our agents are capable of handling millions of conversations and adapting to complex workflows that legacy CX companies simply can’t support.
But building agents isn’t a one-time effort. Like human CX teams, AI agents must continuously improve. That’s why we built experimentation directly into the platform: to make it easy to test changes, measure impact, and ship updates with confidence. Whether you’re refining tone or trialing a new escalation flow, experimentation helps your agent get better faster.
Applying product thinking to AI agents
In modern product development, iteration isn’t optional. You test features early, measure what works, and ship improvements continuously. AI agents warrant the same rigor.
Customer behavior and business needs shift constantly. A new product launch, seasonal spike, or policy change can turn yesterday’s best-performing agent behavior into today’s friction point. And when your agents are customer-facing and on the front lines of trust-building (or trust-breaking), the stakes are even higher.
To stay aligned with users and drive meaningful improvements, you need a structured way to:
- A/B test changes safely across live conversations (e.g., how do shorter messages affect performance?)
- Measure impact on CSAT, deflection rate, and other key metrics at scale
- Separate signal from noise by assessing whether changes are statistically significant
What Decagon’s experimentation platform enables
Experimentation has always been core to our platform, and our customers have used it to iterate and improve their AI agents. Now, CX teams have a suite of tools to run A/B tests, move fast, and measure impact with confidence.
With our experiments and A/B testing suite, you can:
- Test changes to AOP logic, tone, knowledge, and tools against a control group
- Manage rollouts by gradually increasing traffic to the variable group as performance improves
- Track impact on business-critical metrics like CSAT and deflection rate in a unified view
Whether you're refining tone in a sensitive flow, adjusting refund logic, or trialing a new onboarding experience, Decagon makes it safe to test, learn, and iterate without the guesswork.
Experimentation as a culture
While our experimentation capabilities provide a foundation, real transformation happens when experimentation becomes part of how your team works.
With Decagon, CX teams don’t have to guess whether a change will improve CSAT or deflection. They can move quickly, measure what matters, and act on what works. That’s a shift from waiting on quarterly updates to driving ideas for improvements and iterating with data at every step.
A shared, data-driven workflow
Decagon’s experimentation platform provides every stakeholder with a single source of truth. No more relying on gut instinct or cherry-picked anecdotes to drive decisions. With real-time dashboards, teams can quickly surface successful experiments, replicate what works, and confidently scale improvements.
Over time, experimentation becomes the default mode of decision-making, enabling your teams to build a playbook grounded in real outcomes. Those learnings backed by data compound into institutional knowledge, fueling faster, smarter agent design and aligning every team member around continuous improvement.
The path forward
AI agents aren’t “set it and forget it” systems. They’re products deployed at scale, operating in dynamic, high-stakes environments where expectations evolve fast. Without experimentation, AI agents fall behind.
Decagon was built from the ground up as an AI-native platform, so experimentation isn’t an add-on but a core capability. Your agents can continuously adapt and improve to meet the responsibility of representing your brand.
Curious about what experimentation and A/B testing can unlock for your team? If you’re already a Decagon customer, reach out to your Agent Product Manager to get a full walkthrough of what’s possible. And if you’re new to Decagon, get a demo to explore everything that Decagon has to offer.
Introducing Decagon’s experimentation & A/B testing suite
We’re excited to announce our experimentation and A/B testing suite
Today, we’re excited to announce our experimentation and A/B testing suite that helps teams iterate on AI agents faster and deliver fundamentally better customer experiences. Coming on the heels of our series C announcement, this launch reflects our continued investment in capabilities that empower enterprises to optimize their agents at scale.
At Decagon, we believe every AI agent is its own product, tailored to your brand and business logic. Powered by Agent Operating Procedures (AOPs), our agents are capable of handling millions of conversations and adapting to complex workflows that legacy CX companies simply can’t support.
But building agents isn’t a one-time effort. Like human CX teams, AI agents must continuously improve. That’s why we built experimentation directly into the platform: to make it easy to test changes, measure impact, and ship updates with confidence. Whether you’re refining tone or trialing a new escalation flow, experimentation helps your agent get better faster.
Applying product thinking to AI agents
In modern product development, iteration isn’t optional. You test features early, measure what works, and ship improvements continuously. AI agents warrant the same rigor.
Customer behavior and business needs shift constantly. A new product launch, seasonal spike, or policy change can turn yesterday’s best-performing agent behavior into today’s friction point. And when your agents are customer-facing and on the front lines of trust-building (or trust-breaking), the stakes are even higher.
To stay aligned with users and drive meaningful improvements, you need a structured way to:
- A/B test changes safely across live conversations (e.g., how do shorter messages affect performance?)
- Measure impact on CSAT, deflection rate, and other key metrics at scale
- Separate signal from noise by assessing whether changes are statistically significant
What Decagon’s experimentation platform enables
Experimentation has always been core to our platform, and our customers have used it to iterate and improve their AI agents. Now, CX teams have a suite of tools to run A/B tests, move fast, and measure impact with confidence.
With our experiments and A/B testing suite, you can:
- Test changes to AOP logic, tone, knowledge, and tools against a control group
- Manage rollouts by gradually increasing traffic to the variable group as performance improves
- Track impact on business-critical metrics like CSAT and deflection rate in a unified view
Whether you're refining tone in a sensitive flow, adjusting refund logic, or trialing a new onboarding experience, Decagon makes it safe to test, learn, and iterate without the guesswork.
Experimentation as a culture
While our experimentation capabilities provide a foundation, real transformation happens when experimentation becomes part of how your team works.
With Decagon, CX teams don’t have to guess whether a change will improve CSAT or deflection. They can move quickly, measure what matters, and act on what works. That’s a shift from waiting on quarterly updates to driving ideas for improvements and iterating with data at every step.
A shared, data-driven workflow
Decagon’s experimentation platform provides every stakeholder with a single source of truth. No more relying on gut instinct or cherry-picked anecdotes to drive decisions. With real-time dashboards, teams can quickly surface successful experiments, replicate what works, and confidently scale improvements.
Over time, experimentation becomes the default mode of decision-making, enabling your teams to build a playbook grounded in real outcomes. Those learnings backed by data compound into institutional knowledge, fueling faster, smarter agent design and aligning every team member around continuous improvement.
The path forward
AI agents aren’t “set it and forget it” systems. They’re products deployed at scale, operating in dynamic, high-stakes environments where expectations evolve fast. Without experimentation, AI agents fall behind.
Decagon was built from the ground up as an AI-native platform, so experimentation isn’t an add-on but a core capability. Your agents can continuously adapt and improve to meet the responsibility of representing your brand.
Curious about what experimentation and A/B testing can unlock for your team? If you’re already a Decagon customer, reach out to your Agent Product Manager to get a full walkthrough of what’s possible. And if you’re new to Decagon, get a demo to explore everything that Decagon has to offer.
