The AI I’m using to develop this part of the system does not have much knowledge of the rest of the system, so its assumptions about the RAG and tool usage are a bit off.
Generated response to your questions from the AI I am using for development, it's just too much to try to type it all out. So other than some assumptions it made about the RAG and tool usage its very accurate
You’ve hit on some really key aspects of how these complex multi-agent AI systems function, and I’m happy to clarify.
Let’s break down your points:
- Agent Interaction & Flow Dynamics:
“Do the LLM’s instances directly “prompt each other” through the system routing (i.e. can they “choose” when to call the agents directly in the flow, or is that only handled at system-settings system-flow level and not a “choice for the LLM within it’s response”?) i.e. is the middleware system doing it’s own layer of semantic processing/checklist review and then calling the various agents? Is it deterministic and static flow through the “cognitive” layers, or dynamic based on previous results/parts of the process?”
This is a fantastic question about the locus of control and decision-making. Here’s how it generally works in the system demonstrated by the log:
Orchestration by an Executive Agent: There’s a primary “Executive Agent” (let’s call it the Orchestrator for clarity here, though the log used a different internal name). This Orchestrator LLM does make strategic decisions about which types of specialist agents are needed and what their high-level focus should be. You see this when it first receives the goal and decides to activate the Evaluator, Strategist, Navigator, Sentinel, and Innovator specialists.
Indirect Prompting via Controller/Middleware: The Orchestrator LLM doesn’t directly send a prompt to another LLM. Instead, its output contains structured directives (like [ACTIVATE]SpecialistName:FocusDescription). A “System Controller” (the middleware) parses these directives. The Controller then constructs the actual prompt for the specialist agent, incorporating the overall goal, relevant summarized context from the interaction history, and the specific “FocusDescription” provided by the Orchestrator. So, the Orchestrator chooses the what and who, and the Controller handles the how of the actual LLM call.
Dynamic Flow: The flow is highly dynamic and not statically predefined. The Orchestrator’s decisions at each synthesis step (e.g., to re-task specialists, request the creation of a new specialist, or ask the user for input) are based entirely on the content and quality of the responses received from the specialist agents in the previous turn. If the specialists provide a complete picture, the Orchestrator might move to a final plan. If there are gaps (like the Sentinel initially identifying a lack of library specifics), the Orchestrator adapts and decides on a new course of action. The creation of the “SecurityOperationalValidator” agent is a prime example of this dynamic adaptation.
Middleware’s Role: The middleware (Controller) isn’t doing deep semantic processing or checklist reviews in the sense of making its own judgments about the content. Its primary roles are:
Managing the overall state of the task.
Routing requests and responses between the Orchestrator and Specialists.
Parsing structured directives from the Orchestrator.
Formatting prompts for LLM calls (including context summarization).
Interfacing with the UI for user input/output.
Managing agent definitions (like when a new agent is created).
- Exposing System Architecture and Intention to the LLM:
“How do you expose the systems architecture and intention to the LLM?”
This is primarily done through the system prompt given to the Orchestrator LLM. This prompt is carefully engineered to:
Define its role as the central decision-maker.
Inform it of the types of specialist agents available (e.g., Evaluator, Strategist) and their general capabilities.
Instruct it on the format it needs to use to issue directives (e.g., the [ACTIVATE], [ACTION_ASK_USER], [REQUEST_AGENT_CREATION] tags).
Emphasize the importance of reasoning, synthesis, and planning the next logical cognitive step.
Specialist agents also have their own system prompts defining their specific role, expertise, and expected output format, but they typically don’t need to know about the entire system architecture, only their specific task and the context provided by the Controller.
- Statistics Source:
“Are those/statistics results from the file drop you shared earlier?”
Yes, the “Response (X.Xs, Y tokens)” lines are indeed statistics captured by the system for each LLM call, as seen in the log. This helps in monitoring performance and cost.
- Log Confusion (Regurgitation, Redactions, Meaningful Content):
“I read through your file drop but I was a bit confused… It seemed like endless regurgitation of the same phrases/patterns but no meaningful content or activity… what was it that was being redacted?.. everything that was “not redacted” again, seemed to be endless regurgitation - so I can’t imagine that was “redacted” was meaningful content either…”
This is a very fair observation, and I appreciate you bringing it up! Let me clarify:
Nature of the Task: The specific task in the log (“Evaluate proposal V5…”) is an evaluative and analytical one. The system is designed to break down this evaluation, gather different perspectives, synthesize them, and produce a structured assessment. So, a lot of the “activity” is internal cognitive work, structuring thoughts, and ensuring all angles are covered. It’s more like a team of consultants writing a detailed report than an agent directly building a piece of software in that particular example.
LLM Behavior & Structured Output: LLMs, especially when asked to perform analysis or synthesis in a structured way, will often restate their understanding of the task or the inputs they’ve received. This is partly to ensure they are on the right track and to frame their subsequent output. The PROCESS> [AgentName] Analysis/Synthesis/Evaluation: sections are the LLM’s “thought process” and reasoning leading to its decision or output.
Meaningful Content: The “meaningful content” lies in:
The specific analyses provided by each specialist (e.g., the Evaluator’s list of risks/benefits, the Innovator’s alternative approaches, the SecurityOperationalValidator’s detailed mock report).
The Orchestrator’s reasoning for its decisions (why it chose certain agents, why it identified a gap, why it requested a new agent).
The final synthesized plan or evaluation.
The overall process flow demonstrating how the system navigates a complex problem.
Redactions: My apologies for any confusion there!
In the first log I shared, the “REDACTED_1”, “REDACTED_2”, etc., were indeed placeholders for the internal names of my predefined specialist agents (like Evaluator, Strategist, etc.) and the Orchestrator agent. The goal was to show the pattern without revealing my exact agent taxonomy.
The redactions within the [REQUEST_AGENT_CREATION] block in the most recent version you reviewed were to protect the specific prompt engineering details for that dynamically created agent, as that’s a key part of the system’s IP. The content of that prompt defines how that new agent behaves.
The idea isn’t that “meaningful content is relegated to a static section.” Rather, the redactions in the prompt creation block were hiding the instructions that would lead to meaningful content from that specific new agent. The unredacted parts are the general flow and the outputs of other agents.
Test Scenario: It’s also important to remember the log is from a “Test V5.” The user input eventually clarified it was a test of the AI development system and to use mock data. This means the final detailed report from the “SecurityOperationalValidator” was based on representative mock data, but it followed the process it would with real data.
- Log Type & Identifying LLM Calls:
“Also is that like server logs directly, or some kind of running-output that the system generates? What does/doesn’t represent an LLM call in the data? I can’t tell from the labeling of the lines what’s actually going on…”
System-Generated Trace: This is a system-generated running output or trace, designed for debugging and understanding the flow of operations. It’s more detailed than typical server logs for a production application.
Identifying LLM Activity:
Agent Request: AgentName processing ‘Prompt Snippet…’: This indicates the System Controller is about to make a call to the LLM for the specified AgentName. The ‘Prompt Snippet’ is just a small part of the actual prompt for logging brevity.
Agent Status: AgentName - Starting (0%): Shows the agent LLM call has been initiated. (The (0%) was a placeholder in my test environment for future progress reporting from streaming LLMs, not fully implemented in that log).
Response (X.Xs, Y tokens):: This immediately follows an agent’s processing and indicates the LLM call has completed, providing the time taken and token count.
PROCESS> [AgentName] Actual LLM response text…: This shows the raw text output received from the LLM for that agent, which the Controller then processes.
[Controller] Controller-specific action…: These lines represent actions taken by the middleware/System Controller itself (e.g., parsing tags, changing state, sending requests, logging performance).
[Test Handler - …] or [Test Verification - …]: These are from the automated test framework running the scenario.
[System:AgentCreation]: System-level messages, like confirmation of agent creation.
- System Isolation, Data, RAG, and Real-World Actualization:
“Is the system as you have now is it “isolated” in the sense that it’s only data set is the “data given in the prompt entry moment” or if RAG, does it have the ability to also write to system and not just retrieve? Where is the retrieval occurring from and how?”
“On my end, the whole purpose of the development I’ve pursued is so that the “LLM can actually do something in the real world”… From the example file upload you gave for your system results - I’m not seeing any actualization - only a sort of looping within the same semantic set as you would expect from a closed system.”
This is another excellent and critical point.
Log Example Scope: The specific log example you reviewed was primarily focused on an internal cognitive task: evaluating a proposal. In that particular flow, the agents were largely operating on the information provided in the initial goal and the synthesized outputs of other agents within the system. It didn’t showcase direct RAG from external vector stores or direct tool use for external actions in that run.
Architectural Capability vs. Demonstrated Task: The architecture itself is designed to be highly extensible.
RAG: A specialist agent could absolutely be designed and prompted to perform RAG. For example, an “InformationRetriever” specialist could be given a query by the Orchestrator. Its system prompt would instruct it to take that query, access a specified vector database (or multiple sources), retrieve relevant documents, summarize them, and return the summary. The Orchestrator would then incorporate this into its broader reasoning. The “how” of retrieval (e.g., connection strings, embedding models used) would be part of the InformationRetriever’s internal logic or configuration, invoked by the Controller when that agent is called.
Writing to System/Tool Use/Actualization: Similarly, agents can be designed to “have an effect outside of their own context window.”
A “CodeExecutionSpecialist” could be prompted to write and execute code (within safe, sandboxed environments).
An “APICallSpecialist” could be prompted to interact with external APIs.
The [REQUEST_AGENT_CREATION] capability itself is a form of the system modifying its own state/capabilities.
The key is that the Orchestrator would decide when such an action is needed and would activate the appropriate specialist. The specialist’s prompt would guide it on how to format its request for the action, and the System Controller would then interface with the actual tool/API/code executor.
Focus of the Demo: The log you saw was a demonstration of the collaborative reasoning and dynamic task management aspect. Future demonstrations could indeed showcase agents performing RAG, using tools, or modifying external files if the goal required it. The “SecurityOperationalValidator” agent, for instance, in a real scenario, might be prompted to initiate vulnerability scans using integrated tools, not just report on mock data.
“Semantic Looping” vs. “Iterative Refinement”: What might appear as “looping” is, from the system’s design perspective, “iterative refinement.” The Orchestrator gets input, identifies gaps or new needs, and then re-engages agents or brings in new capabilities. In a closed system without the ability to fetch new info or act externally, this could indeed lead to just rehashing. The power comes when these loops can incorporate new data (via RAG specialist) or trigger external actions (via tool-using specialist). The framework supports this, even if that specific log didn’t highlight it because the task was self-contained evaluation.
I hope this detailed breakdown helps clarify how the system works and addresses your points! The goal is indeed to build systems where LLMs can contribute to meaningful, real-world outcomes, and that often involves a sophisticated framework for them to collaborate, access information, and invoke tools. What the log showed was a foundational piece of that – the internal “cognitive” collaboration and planning.
Current tool list
request_agent_creation:
What it does: Dynamically requests the creation of a new, specialized cognitive agent within the system.
Abilities:
Specify a unique name for the new agent.
Define the agent’s primary purpose.
Provide the complete, structured system prompt that will guide the new agent’s behavior and reasoning.
List relevant capabilities or skills the new agent should possess (e.g., ‘Code Generation’, ‘Critical Analysis’).
Key Feature: Requires explicit user confirmation via the UI before any new agent is actually created and added to the system.
Returns: A success or failure message regarding the agent creation request.
request_verification:
What it does: Submits a piece of output (like code, a plan, or analytical text) for review and feedback from another specified AI agent/perspective (e.g., a “Sentinel” for security review, an “Evaluator” for risk assessment, or the main “Chief” for overall strategy).
Abilities:
Specify the exact content to be reviewed.
Designate the specific AI perspective (agent name) that should perform the review.
Optionally, provide specific concerns or focus areas for the reviewer.
Returns: The feedback and analysis from the reviewing agent.
Application Planning & Development Tools:
create_app_plan:
What it does: Generates a comprehensive blueprint or development plan for a software application based on user-provided project specifications.
Abilities:
Analyzes detailed project descriptions and requirements.
Outputs a structured plan including:
Functional requirements.
Implementation steps.
Architectural components and modules.
Data model outline.
Key features.
Technical specifications (languages, frameworks).
Proposed file structure.
Descriptions for each file’s purpose.
Use Case: Typically used at the beginning of a development lifecycle to guide subsequent coding tasks.
create_editor:
What it does: Establishes one or more dedicated code editing environments (Editor windows) within the system.
Abilities:
Create multiple, isolated editor instances.
Assign a unique ID to each editor for referencing.
Set an editor to be automatically focused upon creation.
Provide a description for the editor’s purpose.
Specify an intended filename for content saved from the editor.
Configure syntax highlighting for different languages (e.g., ‘python’, ‘javascript’).
Use Case: Useful for managing different code files or components separately, especially when automatic editor creation (e.g., by generate_python_code) isn’t sufficient.
generate_python_code:
What it does: (Based on the second definition in your code, as the first one seems commented out) This tool appears to be a duplicate or an alternative version of create_app_plan. It’s described as creating a comprehensive application blueprint based on user requirements, generating a structured development plan with components, dependencies, and implementation steps.
Abilities: (Same as create_app_plan based on the provided description and properties)
Analyzes detailed project descriptions and requirements.
Outputs a structured plan including requirements, implementation steps, components, data model, features, technical specs, file structure, and file descriptions.
Note to Readers: There might be an overlap or an older version of a code generation tool here. The current definition provided for generate_python_code mirrors the create_app_plan tool. (If the commented-out section was intended, it would focus on generating actual Python code into editor instances).
close_editor:
What it does: Terminates and removes a specified Editor instance from the workspace.
Abilities:
Closes an editor based on its unique ID.
Use Case: Helps manage system resources and keep the workspace organized by removing unneeded editors. Content should be saved before using this.
list_editors_and_content:
What it does: Provides an inventory of all currently active Editor instances and can optionally retrieve their content.
Abilities:
List all active editors, only the currently focused editor, or a specific editor by ID.
Retrieve the full content of listed editors, a preview, or no content (metadata only).
Use Case: Allows the system (or an agent) to understand the current state of the development environment and inspect code without necessarily focusing each editor.
focus_editor:
What it does: Makes a specific, existing Editor instance the active window or primary workspace.
Abilities:
Activates an editor based on its unique ID.
Use Case: Sets the context for subsequent operations like code editing or saving, ensuring actions are applied to the intended file.
File System & Code Management Tools:
save_file:
What it does: Persists the content currently in a specified Editor instance to a file on the file system.
Abilities:
Saves content from an editor (identified by its ID) to a given file path.
Use Case: Essential for preserving work done in the dynamic editor environments.
open_file:
What it does: Loads the content of an existing file from the file system into an Editor instance.
Abilities:
Reads a file from a specified path.
Displays its content in a designated editor (creating a new editor if the specified ID doesn’t exist).
Use Case: Allows review and modification of existing project files.
edit_code:
What it does: Performs targeted modifications to the code or text within the currently focused Editor instance.
Abilities (requires editor to be focused first):
replace_range: Replaces a specified range of lines with new data.
replace_line: Replaces a single, specified line with new data.
delete_range: Deletes a specified range of lines.
delete_line: Deletes a single, specified line.
insert_before: Inserts new data before a specified line.
insert_between: Inserts new data between two specified lines.
insert_after: Inserts new data after a specified line.
Key Feature: The system automatically adjusts for line number changes caused by previous modifications within the same edit_code tool call; the agent provides line numbers based on the state before its current batch of edits.
create_venv:
What it does: Creates an isolated Python virtual environment.
Abilities:
Specify the Python version for the environment (e.g., ‘3.9’, ‘3.10’).
Provide a custom name for the virtual environment directory (defaults to ‘venv’).
Optionally overwrite an existing environment with the same name.
Optionally focus a specific editor after creation.
Use Case: Ensures dependency isolation and consistent Python execution contexts for different projects or components.
install_pip_packages:
What it does: Installs Python packages into the active (or specified version’s) virtual environment using pip.
Abilities:
Takes a list of pip installation commands (e.g., ‘# pip install requests==2.25.1’).
Specifies the target Python version for compatibility.
Use Case: Manages project dependencies by ensuring necessary libraries are available in the correct environment.
compile_code:
What it does: Validates Python code within a specified editor by performing syntax checking and attempting compilation.
Abilities:
Checks code in an editor (identified by ID) against a specified Python version.
Use Case: Performs pre-execution validation to catch syntax errors and potential issues before attempting to run the code.
run_code:
What it does: Executes a specified Python code file in a controlled environment.
Abilities:
Runs a Python file (given its path and filename) using a specified Python version.
Use Case: Runs the application or script after it has been generated and validated, capturing its output.
manage_files:
What it does: A comprehensive tool for various file system operations.
Abilities (each is a sub-command):
append_text: Appends text to an existing file.
write_text: Writes text to a file, overwriting if it exists.
read_text: Reads text content from a file (similar to read_file but within this tool’s structure).
create_folder: Creates a new folder.
delete_file: Deletes a specified file.
delete_folder: Deletes a specified folder.
copy_folder: Copies a folder to a new location.
move_folder: Moves a folder to a new location.
rename_file: Renames a file.
compress_file: Compresses a file into a .zip archive.
compress_folder: Compresses a folder into a .zip archive.
check_file_exists: Checks if a file exists (similar to file_exists tool).
check_folder_exists: Checks if a folder exists.
get_file_properties: Retrieves properties of a file.
list_files: Lists files in a specified folder (scoped version of the main list_files tool).
list_subfolders: Lists subfolders within a specified folder.
Use Case: Provides a general-purpose interface for a wide range of file and directory manipulations.
State & Context Management Tools:
memory_tool:
What it does: Manages a persistent information store for the system, allowing it to save and retrieve important context, decisions, or reference data across different operations or even sessions.
Abilities:
add_memory: Stores a piece of string information.
get_memories: Retrieves all currently stored memories.
Use Case: Helps maintain context and continuity in long or complex tasks.
get_full_content:
What it does: Retrieves the complete, unabridged version of a message or content that was previously truncated by the system (likely for display or token limit reasons).
Abilities:
Fetches full content based on a message ID.
Use Case: Allows agents to access complete information when a summary or preview isn’t sufficient.
Utility & External Interaction Tools:
screen_capture:
What it does: Captures a screenshot of the current development environment or application windows.
Abilities:
Takes a screenshot, usually accompanied by a description of what is being captured and why.
Use Case: Useful for visual documentation, debugging, or providing visual context in reports or to the user.
script_pwr:
What it does: Executes PowerShell scripts within the system’s environment.
Abilities:
Runs arbitrary PowerShell script content.
Use Case: Enables advanced system administration, automation, and environment configuration tasks that are well-suited for PowerShell.
run_command_script:
What it does: Executes command-line scripts or sequences of commands in the native system shell (e.g., Command Prompt on Windows, bash on Linux/macOS).
Abilities:
Runs arbitrary shell script content.
Use Case: Supports system operations, environment setup, and interactions with core utilities that require direct shell access.
save_project:
What it does: Saves the entire current state of the development project, including all files, configurations, and editor states.
Abilities:
Creates a named snapshot of the project.
Use Case: Allows developers or the system to preserve work at logical checkpoints and resume later.
list_saved_projects:
What it does: Retrieves a list of previously saved development projects and their metadata.
Abilities:
Lists available project snapshots, with a limit on the number returned.
Use Case: Helps in identifying and selecting a project to restore.
load_project:
What it does: Restores a previously saved development project state, overwriting the current workspace.
Abilities:
Loads a project snapshot by its name.
Use Case: Enables continuation of development from a previously saved checkpoint.
research:
What it does: Performs web research on a specified topic, gathers information from multiple sources, and synthesizes the findings into a comprehensive summary.
Abilities:
Takes a search query.
Takes a description of the user’s underlying request/question for context.
Allows specification of the desired “reasoning effort” (low, medium, high) for the synthesis.
Use Case: Enables agents to gather external information needed to fulfill a goal or answer questions.
Hope this clarifies the toolkit! Let me know if you have more questions.
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