Agent Mode Reference

Agent Mode transitions HelseCLI from a reactive assistant into an autonomous developmental system capable of orchestrating complex, multi-stage task chains.

Activation Protocol

Within an active Code Mode session, execute:

> !a

The system interface will indicate that Autonomous Agent Mode is now Active.

Operational Logic

Autonomous Task Orchestration

Upon receiving a high-level objective, the Agent executes a structured logic cycle:

1. Architectural Analysis: Evaluates the existing codebase and project structure.
2. Strategic Planning: Syntheses a prioritized manifest of developmental steps.
3. Autonomous Execution: Orchestrates the appropriate tool modules (e.g., write_file, execute_python, search) sequentially to achieve the objectives.

Dynamic Resolution

The Agent monitor the telemetry from each executed tool. If a command fails (e.g., a unit test regression), the system autonomously analyzes the error state and attempts iterative refinement before proceeding.

System Auditing and Transparency

HelseCLI implements rigorous monitoring protocols for autonomous operations:

1. Implementation Plan Verification

Prior to execution, the Agent presents a formal implementation plan for user review. Users may approve the plan or provide corrective feedback.

2. Action Telemetry (!steps)

Users can retrieve a comprehensive audit log at any time during or after a session using the !steps command. This log provides:

• Action: The specific tool module invoked.
• Payload: The exact code modifications or command arguments.
• Response: The raw system response or computational output.
• Outcome: The specific impact on the project state.

Operational Modifiers

Agent Mode can be further tuned using system modifiers:

Security and Operational Constraints

• Context Preservation: For extremely complex task chains, monitor the LLM context window limits to avoid state degradation.
• Recursive Logic Mitigation: In the event of a recursive failure loop, use Ctrl+C to terminate the autonomous session.
• Execution Environment: It is strictly recommended to utilize Docker Containerization when allowing autonomous systems to execute unverified logic or shell commands.

Implementation Example

Objective: "Develop a modular data analysis utility that extracts content from a defined CSV, synthesizes technical visualizations using matplotlib, and exports the results as a high-resolution PNG asset."