I am experiencing a significant discrepancy in how an Autonomous Node (Gemini 2.0 Flash) processes logic depending on the channel. The bot works great in the Studio Emulator/Webchat but degrades significantly when accessed via the Twilio (SMS) integration. The Goal: Achieve 1:1 behavioral parity between the Studio Emulator and the Twilio production channel. The Problem: In the Studio Emulator, the agent follows the system prompt strictly: it calls tools (KB lookups), performs high-level inference to select KB options, and validates user intent before rendering recommendations. However, via Twilio, the agent exhibits: - Tool-Call Avoidance: A tendency to skip required tool calls and instead repeat/hallucinate responses based on previous turns. - Reduced Inference: Failure to map user "moods" to KB concepts that it identifies easily in the emulator. - Logic Deviation: Ignoring "Grounding Rules" and "Hard Constraints" that are otherwise respected in the webchat. What I’ve Investigated So Far: - Latency/Timeouts: I initially suspected the Twilio 15-second webhook timeout. The theory was that the orchestrator was "rushing" the LLM to respond before the tool call finished to avoid a timeout. - Log Analysis: After inspecting the logs, responses are consistently firing in 2–4 seconds. This suggests the 15s timeout isn't being hit, yet the "panic" behavior (hallucinating/repeating) persists. - Payload Parity: From the logs, the text strings arriving from Twilio appear identical to the webchat strings, yet the model's "reasoning path" changes. ... Continued in system_prompt_sample file. Thanks! https://cdn.discordapp.com/attachments/1478870408475185183/1478870408705867971/Emulator.png?ex=69a9f8e5&is=69a8a765&hm=391b4121b7b8076c845fc9a60a455560de38ec18ecf67e421e7f0557b97cc939& https://cdn.discordapp.com/attachments/1478870408475185183/1478870409041678457/Emulator_2.png?ex=69a9f8e5&is=69a8a765&hm=63d30249654a0b8d3db7cb8ff47d15d122f2d03cc05d42f7e0cdf60e8b5a7af5& https://cdn.discordapp.com/attachments/1478870408475185183/1478870409616036102/Twilio_1.png?ex=69a9f8e5&is=69a8a765&hm=1c2358fb06befd745266b43fa5ed412b00d534265b3f76a293d4707e72e373fb& https://cdn.discordapp.com/attachments/1478870408475185183/1478870409901375548/Twilio_2.png?ex=69a9f8e5&is=69a8a765&hm=e001d67a26a2f63c4ee2f423afdcafe41917100f861063e05d7c7b7e561db373& https://cdn.discordapp.com/attachments/1478870408475185183/1478870410228666530/system_prompt_sample.txt?ex=69a9f8e5&is=69a8a765&hm=2148daeab0752d97726f2c425a06c3bdf0d4a17abeb045977706e86654f13677&

Hey @average-balloon-27662 thank you for reaching out 😄

Thank you for helping out. Yes, I have tried this with my Autonomous Language LLM set to GPT-OSS 120B, Llama 3.3 70B and MyothMax L2 13b (when I frist started the project) and the gap in performance between the channels still persists. Do you think it makes a difference that my Default Best and Default Fast LLM are set to different models (GPT 4o and GPT 4o mini)?

Yes, the faster models provide lesser results. Try changing the models up, test gemini as well and let me know if there is any difference