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Ayush Formulation Verifier with AutoGen Multi-Agent Systems and Resemble AI Voice Interface

Validating traditional Ayush medical formulations against regulatory pharmacopeia requires rigorous verification and accessible citizen interfaces. Build an AutoGen multi-agent system integrated with Resemble AI voice synthesis to parse formulation ingredients, verify compliance against official databases, and voice response summaries.

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What you are building

Develop an AutoGen collaborative agent system where a compliance agent and research agent cross-examine traditional medicine compositions with audio output from Resemble AI.

How work is evaluated

Evaluation

Evaluates AutoGen agents on verifying plant ingredient safety and producing synthetic voice responses.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

toxic_ingredient_detection

Verifies that toxic/unpurified ingredients are correctly flagged.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

verification_precision

Precision of compliance status identification. • target: 0.98 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Implement multi-agent conversations using AutoGen AssistantAgent and UserProxyAgent.

  • Integrate Resemble AI TTS API for generating voice guidance in regional compliance workflows.

  • Establish validation rules against standardized Ayush herbal medicine pharmacopeias.

Resources

Dataset notes

Herbal formulation entries containing botanical Latin names, traditional usages, and regulatory safety limits.

Challenge setup

Evaluates AutoGen agents on verifying plant ingredient safety and producing synthetic voice responses.

Challenge input

JSON with formulation name, botanical ingredient list, and recommended dosage.

AutoGen

Multi-agent conversational structure for compliance cross-examination

Resemble AI

Voice generation for multi-lingual medical guidance

Evaluated output

JSON with safety_status, flag_reasons, and audio_stream_url.

Evaluation checks
  • Verifies that toxic/unpurified ingredients are correctly flagged.
  • Precision of compliance status identification. • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: Ayush AI Safety Benchmark
  • Verification Precision target: 0.98
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Resemble AI · Required
  • AutoGen · Optional
  • n8n · Optional

Evaluation contract

  • toxic_ingredient_detection · Weight 1
  • verification_precision · Weight 1

Metrics

  • verification_precision · Target 0.98

Benchmarks

  • Ayush AI Safety Benchmark
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with formulation name, botanical ingredient list, and recommended dosage.

    Output: JSON with safety_status, flag_reasons, and audio_stream_url.

    Execution harness: Python

Evaluation tools

  • AutoGen · agent_framework
  • Resemble AI · assistants_and_interfaces

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Ayush Formulation Verifier with AutoGen Multi-Agent Systems and Resemble AI Voice Interface