Dynamic Conversation

Microsoft AutoGen Consulting

Build multi-agent conversational frameworks to solve software tasks.

Technical Overview

We use Microsoft's AutoGen framework to build systems where multiple agents chat to solve complex coding, simulation, and data analysis tasks autonomously.

Core Capabilities

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Configuring customizable, conversational multi-agent systems

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Integrating human feedback inside agent conversational loops

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Automating code execution and execution checking in secure sandboxes

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Designing dynamic agent conversation routes based on task states

Key Benefits

  • Supports dynamic, non-linear conversation flow between agents
  • Autonomous code testing: agents write and execute code to check results
  • Capable of solving complex, unstructured math and programming tasks
  • Backed by Microsoft's research and continuous framework upgrades

Integration Blueprint

Our structured methodology to wire and launch technology stacks.

Step 1

Agent Design

Creating assistants, user proxies, and checker agents.

Step 2

Sandbox Setup

Configuring secure Docker runtimes for code execution.

Step 3

Logic Mapping

Writing prompt contexts and task loops.

Step 4

Deployment

Integrating AutoGen into backend pipeline scripts.

Example Implementations

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Use Case 01

Autonomous software development pipelines writing and testing code

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Use Case 02

Financial simulations modeling market reactions with multiple agents

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Use Case 03

Complex math and data engineering pipeline validations

FAQs

Technical answers and support details

What is AutoGen?expand_more

AutoGen is a Microsoft-backed framework for building multi-agent systems that solve tasks via conversational chats.

How is AutoGen different from CrewAI?expand_more

CrewAI uses structured, role-based workflows, while AutoGen focuses on open-ended conversations and sandboxed code execution loops.