focus on isolated tasks or simple prompt engineering. This approach allowed us to build interesting applications from a single prompt, but we are starting to hit a limit. Simple prompting falls short when we tackle complex AI tasks that require multiple stages or enterprise systems that must factor in information gradually. The race toward AGI can be viewed as a scaling of existing model parameters, accompanied by breakthrough architecture, or a multiple model collaboration. While the scaling is expensive and limited to existing model capabilities, and breakthroughs are unmeasurable and can occur at any point in time, multiple model orchestration remains the closest way to build intelligent systems that can perform complex tasks like humans.
One form of intelligence is the ability of…
focus on isolated tasks or simple prompt engineering. This approach allowed us to build interesting applications from a single prompt, but we are starting to hit a limit. Simple prompting falls short when we tackle complex AI tasks that require multiple stages or enterprise systems that must factor in information gradually. The race toward AGI can be viewed as a scaling of existing model parameters, accompanied by breakthrough architecture, or a multiple model collaboration. While the scaling is expensive and limited to existing model capabilities, and breakthroughs are unmeasurable and can occur at any point in time, multiple model orchestration remains the closest way to build intelligent systems that can perform complex tasks like humans.
One form of intelligence is the ability of agents to build other agents with minimal intervention, where the AI has the freedom to act based on request. In this new phase, the machine intelligence handles the complex blueprinting, while the human remains in the loop to ensure safety.
Designing for Machine-to-Machine Integration
We need a standard way for machines to communicate with each other without a human writing custom integrations for every single connection. This is where the Model Context Protocol (MCP) becomes an important part of the stack. MCP serves as a universal interface for models to interact with existing environments, such as calling tools, fetching APIs, or querying databases. While this may look autonomous, a significant amount of manual work is required by the engineer to define the MCP to the model or agent.
Also, a topological framework is essential to guide the logic of agents’ interactions as part of the autonomy journey. Letting agents work in a messy open world leads to hallucinations and a bloating of the required work. However, having a graph-based framework can organize the execution flow. If we treat models as nodes and their interactions as edges, we can start to visualize the dependencies and the flow of data across the entire system. We can build on top of the graph and MCP blueprint to create planner agents that work within the framework to generate blueprints to solve problems by autonomously decomposing complex goals into actionable task sequences. The planner agent identifies what is needed, the graph-based framework organizes the dependencies to prevent hallucinations, and generates agents to achieve your goals; let’s call them “Vibe Agents”.
Intelligence with Vibe Agents
As we transition from an autonomous theory into a complete working system, we will need a way to convert high-level “vibe” statements into executable graphs. The user provides an intent, and the system turns it into a team of agents that collaborate to achieve the outcome. Unlike many multi-agent systems that coordinate through free-form conversation, Vibe Agents operate inside an explicit graph where dependencies and execution paths are structured and observable. This is the problem I have been working to solve as maintainer of the IntelliNode open source framework (Apache license). It is designed around a planner agent that generates the graph blueprint from the user’s intent, then executes it by routing data between agents and collecting the final outputs.
IntelliNode offers a home for Vibe Agents, allowing them not to exist strictly as static scripts but instead act as fluid participants within an evolving workflow.
Vibe Agents created within IntelliNode represent our first experimental attempt to create an autonomous layer. In essence, we want to create a process whereby the definition of each task is being done via declarative orchestration, the description of the desired outcome. By employing this framework, we will allow users to create prompts that allow for orchestrated agents to achieve exceptionally complex tasks versus simple fragmented tasks.
Use Case: The Autonomous Research-to-Content Factory
Illustration of three agents – Photo by author using flaticon
In a traditional workflow, creating a deep dive report or technical article takes substantial effort to compile search results, analyze data, and draft. Within this framework, the bottleneck in the workflow is that every action taken requires input from other layers.
When implementing Vibe Agents, we will be able to establish a self-organizing pipeline that focuses on the use of current live data. When someone requests a high-level intent, they will provide the following single statement: “Research the latest breakthroughs in solid-state batteries from the last 30 days and generate a technical summary with a supporting diagram description”.
How the IntelliNode Framework Executes “Vibe”
Graph of three agents – Photo by author
When the Architect receives this Intent, instead of just producing code, it’s generating a custom Blueprint on-the-fly:
- The Scout (Search Agent): uses google_api_key to perform real-time queries on the internet.
- The Analyst (Text Agent): processes the results of the queries and extracts all technical specifications from the raw snippets
- The Creator (Image Agent): produces the final report, creates a layout or provides a visual representation of the results.
Instead of writing code and creating an API connection to execute your intent, you provide the intent to the machine and it builds the specialized team required to fulfill that intent.
Implementing Using VibeFlow
The following code demonstrates how to handle the transition from natural language to a fully orchestrated search-and-content pipeline.
- OPENAI_API_KEY.
- GOOGLE_API_KEY: From the Google Cloud Console.
- GOOGLE_CSE_ID: The “Search engine ID” from the Search Engine Dashboard.
- GEMINI_API_KEY: For Nano Banana image model.
1. Set up your Environment Set your API keys as environment variables to authenticate the Architect and the autonomous agents.
export OPENAI_API_KEY="your_openai_key"
export GOOGLE_API_KEY="your_google_cloud_key"
export GOOGLE_CSE_ID="your_search_engine_id"
export GEMINI_API_KEY="your_gemini_key"
Install IntelliNode:
pip install intelli -q
2. Initialize the Architect
import asyncio
import os
from intelli.flow.vibe import VibeFlow
# Initialize with planner and preferred model settings
vf = VibeFlow(
planner_api_key=os.getenv("OPENAI_API_KEY"),
planner_model="gpt-5.2",
image_model="gemini gemini-3-pro-image-preview"
)
3. Define the Intent A “Vibe” is a high-level declarative statement. The Architect will parse this and decide which specialized agents are required to fulfill the mission.
intent = (
"Create a 3-step linear flow for a 'Research-to-Content Factory': "
"1. Search: Perform a web research using ONLY 'google' as provider for solid-state battery breakthroughs in the last 30 days. "
"2. Analyst: Summarize the findings into key technical metrics. "
"3. Creator: Generate an image using 'gemini' showing a futuristic representation of these battery findings."
)
# Build the team and the visual blueprint
flow = await vf.build(intent)
4. Execute the Mission Execution handles the orchestration, data passing between agents, and the automatic saving of all generated images and summaries.
# Configure output directory and automatic saving
flow.output_dir = "./results"
flow.auto_save_outputs = True
# Execute the autonomous factory
results = await flow.start()
print(f"Results saved to {flow.output_dir}")
Agent systems are rapidly shifting from “prompt tricks” to software architectures, and the key question is no longer whether multiple agents can work together, than how this cooperation is constrained and replicated in production. Many successful systems use conversation-like agent coordination, which is very useful in prototyping but hard to reason about as workflows become complex. Others take a more advanced workflow approach, such as graph-based execution.
The idea behind Vibe Agents is to compile user’s intent into graphs that can be executed and traced, so that the sequence from start to finish is observable. This means a lot less hand-stitching and more working with the blueprint that this system generates.