[Paper] IACT: A Self-Organizing Recursive Model for General AI Agents: A Technical White Paper on the Architecture Behind kragent.ai

Source: arXiv - 2512.02605v1

Overview

The Interactive Agents Call Tree (IACT) is a new architectural blueprint for building truly autonomous AI agents that can grow and re‑configure themselves on the fly. Instead of wiring a fixed workflow or hand‑crafting a static graph of function calls, IACT lets a high‑level user goal drive the creation of a recursive, dialogue‑based network of sub‑agents that adapts its complexity to the problem at hand. The white‑paper behind kragent.ai shows how this approach can keep agents robust in the face of ambiguous inputs and runtime errors.

Key Contributions

• Self‑organizing agent topology – agents expand a dynamic call tree recursively, matching the structural depth of open‑ended tasks.
• Bidirectional, stateful dialogues – replaces brittle one‑way function calls with interactive “conversation” loops that allow error correction and clarification.
• Interactional redundancy – multiple dialogue paths provide fallback mechanisms, reducing the impact of a single failure.
• Production‑grade deployment – the paper documents real‑world usage of IACT in the kragent.ai platform, offering qualitative insights from live workflows.
• General‑purpose applicability – the model works without any pre‑defined graph or specialized programming, making it usable for a wide range of AI‑driven services.

Methodology

1. Goal Ingestion – a user supplies a high‑level objective (e.g., “plan a multi‑city marketing campaign”).
2. Recursive Expansion – the system spawns an initial “root” agent that parses the goal and decides whether to solve it directly or decompose it. If decomposition is needed, it creates child agents, each responsible for a sub‑task (e.g., “research target demographics”, “design ad creatives”).
3. Dialogue‑Based Interaction – instead of calling a child agent with a single request and waiting for a static response, the parent and child engage in a back‑and‑forth conversation. Each turn carries state, allowing the agents to ask clarifying questions, request missing data, or propose alternative solutions.
4. Interactional Redundancy – multiple dialogue threads can be opened in parallel for the same sub‑task. If one thread hits an error or dead‑end, another can continue, and the system can merge the best outcomes.
5. Dynamic Pruning – once a sub‑task is resolved, the corresponding branch of the call tree is collapsed, and the results are propagated upward. The tree thus grows and shrinks adaptively during execution.

The entire process is orchestrated by a lightweight runtime that tracks agent states, message histories, and error signals, enabling real‑time correction without restarting the whole workflow.

Results & Findings

• Qualitative robustness – In several production scenarios (e.g., automated research pipelines, multi‑step code generation, and customer support orchestration), IACT handled ambiguous user inputs gracefully by prompting for clarification instead of failing outright.
• Scalable complexity – The recursive topology allowed the system to tackle tasks that would have required dozens of hard‑coded steps, all generated on demand.
• Error mitigation – Interactional redundancy reduced the observable failure rate by roughly 30 % compared with a baseline of unidirectional function calls in internal benchmarks.
• Developer productivity – Teams reported faster prototyping cycles because they no longer needed to pre‑define the exact sequence of agent calls; the IACT engine inferred the workflow automatically.

Practical Implications

• Plug‑and‑play AI services – Developers can expose a single “goal” endpoint and let IACT orchestrate the necessary sub‑agents, dramatically lowering integration effort for complex AI pipelines.
• Adaptive automation – Enterprises can automate processes that evolve over time (e.g., regulatory compliance checks) without constantly rewriting workflow scripts.
• Improved user experience – End‑users benefit from agents that ask follow‑up questions instead of returning cryptic errors, leading to higher satisfaction in chat‑bots, virtual assistants, and self‑service portals.
• Reduced maintenance overhead – Because the call tree is generated at runtime, updating a single sub‑agent automatically propagates improvements throughout all dependent workflows.
• Cross‑domain reuse – The same IACT core can coordinate language models, retrieval‑augmented generators, tool‑calling APIs, and even non‑AI micro‑services, making it a universal orchestration layer.

Limitations & Future Work

• Lack of quantitative benchmarks – The paper focuses on qualitative case studies; systematic performance metrics (latency, cost, success rate) are still needed for broader validation.
• Potential overhead – Maintaining multiple dialogue threads and stateful contexts can increase compute and memory usage, especially for very deep call trees.
• Complexity of debugging – While the system self‑corrects, tracing the exact decision path through recursive dialogues can be non‑trivial for developers.
• Future directions include formalizing evaluation protocols, optimizing the runtime for large‑scale deployments, and adding tooling for visualizing and debugging the dynamic call tree in real time.

Authors

• Pengju Lu

Paper Information

• arXiv ID: 2512.02605v1
• Categories: cs.AI, cs.MA, cs.SE
• Published: December 2, 2025
• PDF: Download PDF