Open-source cycle for self-improving AI agents: analyze → improve → simulate.
Joined July 2025
- Tweets 17
- Following 17
- Followers 8
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7 Photos and videos
4 Dec 2025
Simulation caught the edge cases humans missed - escalation rate dropped 21% before we even touched production.
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28 Nov 2025
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Most AI agents aren’t “bad.”
They’re just trained on vibes instead of data.
2025 is the year we stop tuning by intuition.
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28 Nov 2025
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If your “optimization strategy” is:
• tweak prompt
• wait
• pray KPIs go up
…you don’t have an optimization strategy. You have a ritual.
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25 Nov 2025
🚀 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/agent…
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20 Nov 2025
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
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18 Nov 2025
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.…
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29 Jul 2025
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🤖 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.
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29 Jul 2025
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🚀 Try Agentune now:
📦 pip install agentune-simulate
🔗 github.com/SparkBeyond/agent…
Open-source. Real-world ready.
Built to help agents actually perform.
#AgentOptimization #LLMops #AIagents
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29 Jul 2025
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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.
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29 Jul 2025
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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
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29 Jul 2025
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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.
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29 Jul 2025
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🔬 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.
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29 Jul 2025
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Agentune-Simulate lets you:
✅ Simulate real-world convos
✅ Expose weak spots
✅ Grade performance with intent KPI metrics
✅ Run locally in seconds
🔗 github.com/SparkBeyond/agent…
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29 Jul 2025
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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.
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29 Jul 2025
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💥 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
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29 Jul 2025
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🧠 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.
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