From Agent Loops to Deterministic Graphs: Execution Lineage for Reproducible AI-Native Work

2026-05-07

AI Summary

This work addresses the limitations of existing AI agent workflows, which rely on implicit dialogue states and struggle to ensure stability of intermediate artifacts, isolate irrelevant updates, and propagate changes precisely. To overcome these challenges, the paper proposes modeling AI-native workflows as directed acyclic graphs (DAGs) and introduces the concept of execution lineage. By leveraging explicit dependency tracking, identity-based identification of intermediate artifacts, and an identity-aware replay mechanism, this approach achieves deterministic computation graphs in AI agents for the first time. The method guarantees precise change propagation, zero contamination across unrelated branches, and preserves both upstream stability and cross-artifact consistency. Evaluated on a policy memo updating task, DAG-based replay attains 100% fidelity in final outputs, substantially outperforming iterative baseline approaches.

Abstract

Large language model systems are increasingly deployed as agentic workflows that interleave reasoning, tool use, memory, and iterative refinement. These systems are effective at producing answers, but they often rely on implicit conversational state, making it difficult to preserve stable work products, isolate irrelevant updates, or propagate changes through intermediate artifacts. We introduce execution lineage: an execution model in which AI-native work is represented as a directed acyclic graph (DAG) of artifact-producing computations with explicit dependencies, stable intermediate boundaries, and identity-based replay. The goal is not to make the model a better one-shot writer, but to make evolving AI-generated work maintainable under change. We compare execution-lineage replay against loop-centric update baselines on two controlled policy-memo update tasks. In an unrelated-branch update, DAG replay preserved the final memo exactly in all runs, with zero churn and zero unrelated-branch contamination, while loop baselines regenerated the memo and frequently imported unrelated context. In an intermediate-artifact edit, all systems reflected the new constraint in the final memo, but only DAG replay achieved perfect upstream preservation, downstream propagation, unaffected-artifact preservation, and cross-artifact consistency. These results show that final answer quality and maintained-state quality are distinct. Strong loop baselines can remain competitive at producing polished final outputs when the task is a bounded synthesis/update problem and all current sources fit in context, but immediate task success can mask partial state inconsistency that may compound over future revisions. Execution lineage provides stronger guarantees about what should change, what should remain stable, and how work evolves across revisions.

Problem

• execution lineage
• reproducibility
• AI-native work
• state consistency
• artifact evolution

Innovation

• execution lineage
• directed acyclic graph
• artifact consistency
• identity-based replay
• AI-native workflows