TOTemitayo Ogunsusi
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Preprint · 2026

UA-ChatDev

A multi-agent software framework that detects uncertain intermediate outputs and selectively verifies them before errors propagate.

Focus

Agentic AI · Uncertainty quantification · Retrieval · Evaluation

Status

Preprint

Year

2026

Uncertainty-aware collaboration

Know when to ask for more.

INPUTBuild from a task requirement↴
  1. 01Design
  2. 02Code
  3. 03Complete
  4. 04Review
  5. 05Test
u
One question at every stage.Does uncertainty exceed the threshold?
NO →ContinuePass the output forward
YES ↴Retrieve contextInform the next phase
↓ Generated code & testsInspectable from start to finish
Explore the process
  1. A task enters Design → Coding → Code Completion → Code Review → Testing.
  2. Instructor and assistant pairs include CEO / CTO, CTO / Programmer, Reviewer / Programmer, and Tester / Programmer.
  3. At each phase, token log probabilities feed an uncertainty score compared with a phase-specific threshold.
  4. High uncertainty triggers external retrieval. Output and available context pass into subsequent phases.
  5. The demo displays generated code and tests, token streams, retrieval evidence, and heuristic quality checks. It does not execute generated tests.

Inside the live factory

Watch uncertainty change the workflow.

A task moves through five phases: Design, Coding, Code Completion, Code Review, and Testing. Each pairs an instructor with an assistant: CEO and CTO, CTO and Programmer, Reviewer and Programmer, or Tester and Programmer.

  1. 01

    GenerateAgents produce a response for the current development phase.

  2. 02

    Measure uncertaintyToken log probabilities feed a rolling score, compared with that phase’s threshold.

  3. 03

    Retrieve when neededA score above the threshold triggers a web lookup. Retrieved context informs subsequent phases.

  4. 04

    Continue downstreamThe next agent receives the accumulated output and available retrieved context.

The demo makes these events inspectable through the token stream, uncertainty gauge, retrieval sources, and event log. Select a task and press Play to follow a run.

The prototype uses heuristic uncertainty scores. Generated code and tests are displayed, not executed; completion metrics are static quality checks, separate from the paper’s evaluation.

Explore live demo ↗

01 / Research question

How can multi-agent coding systems identify unreliable decisions before hallucinations move from planning into code and testing?

02 / Method

  1. 01

    Estimate response uncertainty from token-level log probabilities at critical phase transitions.

  2. 02

    Calibrate phase-aware thresholds and trigger retrieval-based verification only when uncertainty is high.

  3. 03

    Evaluate completeness, executability, consistency, overall quality, ablations, and agent communication on SRDD.

03 / Outcome

The study reports consistent gains over single- and multi-agent baselines, with the strongest improvements appearing in execution reliability and overall quality.

Continue exploring

From experiments to systems.

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