For teams that ship agent changes more than once a week
You shipped 5 changes this week.Which one moved the metric?
New LLM, different library, rewritten step, tweaked prompt. You changed everything at once. Now something improved - and you have no idea what caused it.
Free tier · Test any variable in production · Statistical confidence built in
Building in production with teams like these
Evidence, not intuition.
You changed the LLM and the prompt in one deploy. Something improved. You don't know which.
Each variable is a separate variant. You know exactly which change caused which outcome.
Manual evaluation covers 20 outputs out of 20,000. Not representative.
Real production traffic across every variant, on your actual user distribution.
A bad model swap or library upgrade hits 100% of users simultaneously.
Smart traffic splitting catches regressions at 5% exposure before they reach everyone.
"This version feels better" is your current test methodology.
Statistical significance required before any variant is declared a winner.
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Two-thirds of teams shipping AI in production make changes based on intuition - not controlled experiments. The gap between the best and worst AI teams is almost entirely in this number.
Shipping changes and calling it a process.
You change everything at once
Swapping GPT-4 for Claude, upgrading LangChain, rewriting a pipeline step - when multiple variables change in a single deploy, any outcome improvement is unattributable. You learned nothing you can repeat.
Manual eval covers 0.1% of real traffic
You review 20 outputs. Your agent processes 20,000. The sample you checked isn't representative of edge cases, low-frequency inputs, or the exact distribution your users actually send.
Big-bang deploys mean big-bang risk
Every change ships to 100% of production simultaneously. A bad LLM swap, a regressed prompt, a broken library update - it hits every user at once before you catch it.
“Your data team demands p < 0.05 before shipping a feature. Your AI team ships model swaps, library upgrades, and agent rewrites based on vibes.”
Every uncontrolled deploy is a lost learning opportunity. You shipped 5 this week.
Run your first experimentYour competitors run 50 experiments a month.
The question isn't just which prompt performs better. It's which LLM handles your edge cases best. Which retrieval library reduces hallucination for your specific data. Whether a single orchestrating agent or a multi-agent pipeline produces better outcomes. Whether your routing logic should use rule-based trees or let the model decide. These are all testable variables - and almost no one is running controlled experiments on any of them.
“The best version of your agent isn't the one that feels best in a code review. It's the one your production traffic proved is best.”
The next change you ship without an experiment is a guess you could have turned into data.
Tag any variable. Measure everything. Ship what wins.
Tag any variable as a variant - one line of code
Prompts, model configs, library choices, pipeline steps, routing logic, multi-agent topology - wrap any variable in a variant tag using the Syrin SDK. No separate platform. Experiments live in your codebase alongside your agents.
Live traffic splits automatically across variants
Syrin routes production traffic across your variants using smart allocation - sending more to better-performing variants as evidence accumulates. You don't manually adjust split percentages. The system learns and routes as it runs.
Ship when significance is confirmed - not when you hope
When statistical significance is reached, Syrin tells you which variant won, by how much, on which metric, with what confidence level. Ship to 100% with evidence. No more gut-feel deploys, no more attribution guesswork.
Not ready for Agent Experimentation yet?
Tell us what's missing.
Share your exact situation. If you need something we haven't built for Agent Experimentation, tell us - we'll build it or show you a solution today.
Share your situation - we read every response
Book 20 min with our team
No sales deck. No pitch. We get on a call, understand your agent setup, and either show you how Syrin solves it right now - or take your problem back to our engineers and build it.
Book a free callWe have shipped features in <2 weeks after a single user call. If your problem is real and affects other teams running agents in production, it goes on our roadmap - and you'll be the first to use it.
From 'it feels right' to 'the data agrees.'
When any variable is testable, teams run more experiments. More experiments means faster compounding improvement across every layer of the stack.
Every performance change is tied to a specific variable - prompt, model, library, step, or logic. You always know what caused what.
The same standard your data team holds themselves to, applied to every agent change.
Teams running controlled experiments improve agent performance 3–5× faster than teams iterating on intuition.
Your next deploy reaches everyone. Know it works first.
Run an experiment instead. Ship what the data says wins.
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