AutoGen vs DeepEval for real-time apps: Which Should You Use?

AutoGen and DeepEval solve different problems. AutoGen is for orchestrating multi-agent LLM workflows; DeepEval is for evaluating LLM outputs, pipelines, and agent behavior with test-style assertions. For real-time apps, use AutoGen when you need the system to act, and DeepEval when you need the system to prove it still works.

Quick Comparison

When AutoGen Wins

Use AutoGen when the product itself needs to coordinate multiple steps or roles in real time.

• •

  You need a live planner-executor pattern
  Example: a support assistant that receives a customer issue, drafts a response, checks policy constraints with another agent, then sends a final answer.

• •

  You want tool-heavy workflows with branching behavior
  AutoGen’s AssistantAgent plus tool registration is a clean fit when one agent calls APIs, another validates output, and a third handles escalation.

• •

  You are building human-in-the-loop systems
  UserProxyAgent is useful when the workflow must pause for approval before continuing, which is common in banking ops and insurance claims.

• •

  You need multi-agent collaboration instead of single-call prompting
  If one model call is not enough and you actually need debate, critique, summarization, or role separation, AutoGen gives you that structure.

A concrete example: an underwriting assistant that collects applicant data, asks follow-up questions through one agent, checks policy rules through another agent, then prepares a recommendation for an underwriter to approve.

When DeepEval Wins

Use DeepEval when the product already exists and you need confidence that changes do not break it.

• •

  You want automated regression tests for LLM features
  DeepEval fits directly into CI with test cases like:

  from deepeval import assert_test
  from deepeval.test_case import LLMTestCase
  from deepeval.metrics import AnswerRelevancyMetric
  
  test_case = LLMTestCase(
      input="What is my policy deductible?",
      actual_output="Your deductible is $500."
  )
  
  assert_test(test_case=test_case, metrics=[AnswerRelevancyMetric()])
  

• •

  You care about RAG quality
  Metrics like FaithfulnessMetric and context-aware checks help catch hallucinations before they hit users.

• •

  You need release gates on prompt changes
  If your team ships prompt updates weekly, DeepEval gives you measurable thresholds instead of gut feel.

• •

  You want observability without turning your app into an agent swarm
  DeepEval helps validate output quality without adding runtime complexity to the request path.

A practical example: an insurance claims chatbot where every new prompt version must pass faithfulness checks against policy documents before deployment.

For real-time apps Specifically

Pick AutoGen only if the real-time app’s core value depends on live orchestration across multiple agents or tools. Otherwise pick DeepEval as the default companion because it keeps latency low in production while giving you hard quality gates before release.

For most real-time apps in finance and insurance, the winning setup is: build the workflow with your normal app stack or a minimal orchestration layer, then use DeepEval to test every prompt change, retrieval change, and model swap before it reaches production. That gives you speed at runtime and control at deploy time—the combination that actually matters.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.