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

Pengju Lu

TL;DR

IACT presents a self-organizing recursive topology for general AI agents that replaces static workflows with dynamic, bidirectional dialogues to manage open-ended tasks. It details a macro- and micro-architecture, including dynamic context construction, a perception–action loop, and a unified Extended Markdown protocol, all geared toward mitigating error propagation and context degradation. The paper reports qualitative evidence from production deployments (kragent.ai), case studies, and security-focused system design, arguing that dynamic topology and runtime tool synthesis enable reliable, scalable autonomous workflows today. Collectively, IACT argues for a shift from pre-defined scripts to adaptive, agentic computing that grows its structure as tasks demand, with practical implications for engineering, research automation, and AI alignment.

Abstract

This technical white paper introduces the Interactive Agents Call Tree (IACT), a computational model designed to address the limitations of static, hard-coded agent workflows. Unlike traditional systems that require pre-defined graphs or specialized programming, IACT operates as a general-purpose autonomous system driven purely by user dialogue. Given a high-level objective, the system autonomously grows a dynamic, recursive agent topology incrementally tailored to the problem's structure. This allows it to scale its organizational complexity to match open-ended tasks. To mitigate the error propagation inherent in unidirectional function calls, IACT introduces interactional redundancy by replacing rigid invocations with bidirectional, stateful dialogues. This mechanism enables runtime error correction and ambiguity resolution. We describe the architecture, design principles, and practical lessons behind the production deployment of this model in the kragent.ai system, presenting qualitative evidence from real-world workflows rather than exhaustive benchmark results.

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

TL;DR

IACT presents a self-organizing recursive topology for general AI agents that replaces static workflows with dynamic, bidirectional dialogues to manage open-ended tasks. It details a macro- and micro-architecture, including dynamic context construction, a perception–action loop, and a unified Extended Markdown protocol, all geared toward mitigating error propagation and context degradation. The paper reports qualitative evidence from production deployments (kragent.ai), case studies, and security-focused system design, arguing that dynamic topology and runtime tool synthesis enable reliable, scalable autonomous workflows today. Collectively, IACT argues for a shift from pre-defined scripts to adaptive, agentic computing that grows its structure as tasks demand, with practical implications for engineering, research automation, and AI alignment.

Abstract

This technical white paper introduces the Interactive Agents Call Tree (IACT), a computational model designed to address the limitations of static, hard-coded agent workflows. Unlike traditional systems that require pre-defined graphs or specialized programming, IACT operates as a general-purpose autonomous system driven purely by user dialogue. Given a high-level objective, the system autonomously grows a dynamic, recursive agent topology incrementally tailored to the problem's structure. This allows it to scale its organizational complexity to match open-ended tasks. To mitigate the error propagation inherent in unidirectional function calls, IACT introduces interactional redundancy by replacing rigid invocations with bidirectional, stateful dialogues. This mechanism enables runtime error correction and ambiguity resolution. We describe the architecture, design principles, and practical lessons behind the production deployment of this model in the kragent.ai system, presenting qualitative evidence from real-world workflows rather than exhaustive benchmark results.

Paper Structure

This paper contains 42 sections, 4 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Design Philosophy
  3. Dynamic Context Construction
  4. LLM-Centric Control Flow
  5. Macro-Architecture: The IACT Model
  6. The Derivation of Topology
  7. From Function Tree to Dialogue Tree
  8. Contextual Isolation and Cognitive Efficiency
  9. Sequential Execution Model
  10. Topology as Implicit Memory Hierarchy
  11. Control Flow Patterns
  12. Micro-Architecture: The Agent Node
  13. The Agent Loop
  14. Hybrid Language Interpreter
  15. The Symbolic Variable Mechanism
  16. ...and 27 more sections

Figures (2)

  • Figure 1: The Agent Micro-Architecture. The diagram visualizes the cyclic flow between the LLM (Brain) and the Interpreter (Executor). The Interpreter executes the formal code generated by the LLM, and the execution results (Feedback) are fed back to the Context, creating a closed-loop cognitive cycle.
  • Figure 2: State-Machine-Based Tooling Paradigm. The diagram illustrates the Browser Tool as a state machine. The available action space (Prompt) changes dynamically based on the tool's state, reducing cognitive load and hallucination.