Agentune

Simulation caught the edge cases humans missed - escalation rate dropped 21% before we even touched production.

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2️⃣ If your “optimization strategy” is: • tweak prompt • wait • pray KPIs go up …you don’t have an optimization strategy. You have a ritual.

1️⃣ Most AI agents aren’t “bad.” They’re just trained on vibes instead of data. 2025 is the year we stop tuning by intuition.

🚀 New: Agentune Walkthrough We just dropped a quick demo showing how Agentune helps you evaluate, stress-test, and improve your AI agents with realistic simulations + data-backed insights. 🔗 GitHub: github.com/SparkBeyond/ag…

Most teams still “tune” their AI agents with guesswork. They nudge prompts. Swap models. Hope KPIs move. Open-source needs better than intuition. Agentune Analyze & Improve takes real conversations → finds what actually moves CSAT, resolution, and conversion. Evidence > vibes

Most teams still tune AI agents by intuition. Agentune Analyze & Improve identifies the conversation patterns that actually move CSAT, resolution, or conversion — and validates changes in simulation before rollout. Details here: sparkbeyond-staging.webflow.io/articles/agent…

10/ 🚀 Try Agentune now: 📦 pip install agentune-simulate 🔗 github.com/SparkBeyond/ag… Open-source. Real-world ready. Built to help agents actually perform. #AgentOptimization #LLMops #AIagents

🧵1/ 🤖 Your AI agent aced the sandbox test. 💥 Then it met real users—and fell apart. Why? Because real-world performance ≠ prompt engineering. Meet Agentune — an open-source engine that stress-tests, analyzes & optimizes AI agents like teammates.

9/ Agentune isn’t just a tool. It’s a shift in mindset: From prompt engineers → performance engineers. From LLM wrappers → agent systems. From guessing → simulating, analyzing, and iterating.

8/ Coming soon: 🧠 Agentune-Analyze – Root cause mining – Driver discovery – KPI-based insights Built on SparkBeyond’s tech used to: – Cut churn 30% for a media giant – Optimize 600 store locations at Zabka

7/ Most teams still optimize agents by intuition. But gut-feel = biased. Anecdotes ≠ scale. Unmeasured changes hide failure. Agentune turns ops into science: test, measure, improve—repeat.

6/ 🔬 The Agent Optimization loop: Analyze → cluster convos, track KPIs, find failure causes Improve → tweak prompts, tools, policies Evaluate → test in lab, monitor regressions Then repeat—with data.

5/ Agentune-Simulate lets you: ✅ Simulate real-world convos ✅ Expose weak spots ✅ Grade performance with intent + KPI metrics ✅ Run locally in seconds 🔗 github.com/SparkBeyond/ag…

4/ Why do great LLMs ship mediocre agents? Because prompt tuning ≠ performance tuning. Live environments are messy: – Partial refunds – Regional laws – Budget users And your agent needs to learn from success and failure.

3/ 💥 Just launched: Agentune-Simulate An open-source engine that stress-tests your agent in a safe lab: – Synthetic users – Edge cases – Realistic multi-turn dialogue – Clear metrics Install: pip install agentune-simulate

2/ 🧠 Agentune brings structure to agent performance with a tight feedback loop: Analyze → Improve → Evaluate Think of it like coaching a human rep—only faster, scalable, and data-driven.