cs.AIOct 15, 2026

Improving Environment-Agnostic Agents: The Role of Scaffolding

Authors: Sergey Rodionov

Organizations: SingularityNET

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Abstract

Agents built on stronger language models may increasingly construct useful procedures for themselves, reducing the advantage of developer-supplied scaffolding. We investigate why this advantage persists and whether an environment-agnostic agent can be improved without environment-specific engineering. We evaluate a Codex-based agent in two nearly identical prompt variants on ARC-AGI-3, AGI Maze, and Terminal-Bench. Each variant is deployed with unchanged code, prompts, and general-purpose tools across environments, receiving environment-specific information as input. Changing only an instruction to encourage reporting difficulties and returning control for automatic continuation improves mean scores on both ARC-AGI-3 and AGI Maze, while Terminal-Bench shows small, mixed changes. Yet the general agent remains below an ARC-AGI-3-specific reference, including with GPT-5.6-sol. The reference prescribes organization and control rather than supplying new game rules or solutions. To investigate this gap, we transfer reset decisions to a scripted controller that follows a convention already given to the agent. This small change recovers part of the specialized agent's advantage. A complementary AGI Maze experiment shows that additional tutorial experience can reduce performance. Together, these findings highlight the distinction between having information and tools available and using them effectively. Supplied procedures still matter for the models tested here. The performance gains also come with higher token-based cost. The experiments do not separate better use of information and procedures from additional or differently allocated inference. We provide the EnvTraverse framework and an evaluated baseline agent for testing whether further environment-independent changes improve performance in some environments without degrading it in others.

Figures & tables

Figure 1: Common agent–environment interaction contract. At initialization, the environment supplies a textual description of the task and interaction interface, optionally accompanied by files. The agent then submits JSON actions and receives observation files and terminal status. The agent remains unchanged across environments, while each environment defines its own actions, observations, and termination conditions.
Figure 2: Architecture shared by the two prompt variants. The scripted controller sends prompts and checks generic session state. Codex performs the task-solving work and uses the provided client to interact with the environment. The client is part of the agent and saves the environment description, actions, and observations. All components remain unchanged across environments.
Figure 3: Scripted controller used by both prompt variants. The initial prompt includes the selected continuation instruction. State is inspected after Codex returns; “No progress” means no new action since the preceding continuation. “End” denotes controller exit after environment termination or a return with no new action following the stuck reminder. The runtime-recovery supervisor is omitted; its handling of controller exits is described in the text.
Figure 4: Mean ARC-AGI-3 RHAE over retained evaluation blocks. Solid lines show the environment-agnostic variants Baseline and Stop-and-report; dashed lines show Controlled_RESET and twma_v1.61 . The four horizontal positions are discrete model–effort configurations, not a calibrated capability scale; connecting lines only aid comparison. Each point averages three blocks, except twma_v1.61 with GPT-5.6-sol max, which averages two. Individual runs and exclusions are reported in Appendix C.2 .
Figure 5: AGI Maze mean RHAE70; higher is better. Solid lines show single-labyrinth results, and dotted lines show the tutorial-set curriculum. Blue circles and triangles denote Baseline; orange squares and diamonds denote Stop-and-report. Each point averages three complete 106-target evaluation blocks, except Stop-and-report with curriculum at GPT-5.5 xhigh and GPT-5.6-sol max, which average two. Model–effort configurations are discrete. Individual execution batches and exclusions are given in Appendix C.3 .
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