Recoleta
GitHub
Source note

Position: agentic AI orchestration should be Bayes-consistent

Authors
Theodore Papamarkou, Pierre Alquier, Matthias Bauer, Wray Buntine, Andrew Davison, Gintare Karolina Dziugaite, et al.
Source
arXiv
Published
Collection
Software Intelligence
Agent OrchestrationBayesian Decision TheoryMulti Agent SystemsCode IntelligenceHuman AI Interaction
Open arXivMarkdown source

Summary

The paper argues that agentic AI systems should put Bayesian decision rules in the orchestration layer, while leaving LLMs and tools as black-box predictors. It targets routing, stopping, escalation, and budget choices where uncertainty, cost, and risk shape the best action.

Problem

  • LLM agents often must choose which tool or expert to call, whether to ask the user, when to stop, and how much compute to spend; token probabilities do not map cleanly to task-level uncertainty.
  • This matters in multi-step or high-stakes systems because bad routing, weak stopping rules, and repeated tool calls can waste budget, add latency, or raise safety risk.
  • Making the LLM itself a full Bayesian updater is expensive and may still miss the uncertainty that matters for action selection.

Approach

  • Add a Bayesian controller above LLMs and tools. It tracks a posterior over latent task variables such as whether code will pass tests, which hypothesis is best supported, or which agent is reliable.
  • Treat each agent message or tool output as noisy evidence. In the code example, the controller updates r_t(y) ∝ r_{t-1}(y) p_i(z_t|y)^{α_i}, where α_i tempers the evidence from agent i.
  • Choose the next action by posterior expected utility or value of information. The controller calls another agent only when the expected gain exceeds cost c_i; otherwise it can stop, ask, abstain, or escalate.
  • Use recalibration, likelihood tempering, dependence-aware evidence pooling, and escalation when observation models are misspecified or repeated calls produce correlated evidence.
  • The paper illustrates the idea with multi-agent code generation/testing, multi-agent hypothesis discussion, and cross-task routing based on learned competence parameters.

Results

  • The paper reports 0 experiments, 0 datasets, and 0 empirical benchmark comparisons. It is a position paper, so it does not claim a measured accuracy, pass-rate, cost, or latency gain.
  • Its main claimed result is a design argument: Bayesian control can be applied at 1 orchestration layer without making LLM parameters Bayesian.
  • It lists 7 desired properties for practical Bayesian control: cost and utility modeling, low-overhead decisions, compact interaction history, human-AI and multi-agent integration, typed software interfaces, multimodal inputs, and simple user controls.
  • It gives 3 concrete design examples: code-generation testing, hypothesis deliberation, and cross-task competence routing.
  • In the code example, the controller manages n agents, a binary task outcome Y ∈ {0,1}, per-agent costs c_i, reliability weights α_i, and the belief update in Equation (1).

Link