Introduction
In the rapidly evolving landscape of generative artificial intelligence, the ability to build systems that can reason, act, and adapt autonomously is becoming a cornerstone of next‑generation applications. While large language models provide the raw linguistic power, they lack a structured framework for orchestrating external tools, enforcing safety constraints, and scaling to complex workflows. The control‑plane architecture fills this gap by acting as a central nervous system that coordinates the various moving parts of an agentic AI. This tutorial walks through the design and implementation of such a system, emphasizing safety, modularity, and scalability. By the end of the guide you will understand how to break down an agentic AI into discrete, testable components, how to embed safety rules into the orchestration logic, and how to extend the system with new tools or retrieval services without disrupting the core loop.
The core idea is simple: separate the what from the how. The control plane defines the high‑level reasoning strategy—what decisions need to be made, what information is required, and how to evaluate the outcomes—while the worker plane implements the concrete actions, such as calling an API, querying a database, or performing a calculation. This separation mirrors the classic controller‑model pattern in software engineering and brings several practical benefits. First, it makes the system easier to test because each component can be exercised in isolation. Second, it allows developers to swap in more powerful tools or newer models without rewriting the orchestration logic. Third, it provides a natural place to enforce safety rules, such as content filtering or rate limiting, because all tool calls pass through a single, observable channel.
Throughout the post we will build a miniature retrieval‑augmented agent that can answer user queries, fetch relevant documents, and summarize the findings. The example is intentionally lightweight so that readers can replicate the code in a few minutes, but the architectural patterns we discuss scale to enterprise‑grade deployments that handle thousands of concurrent users and integrate dozens of specialized services.
Main Content
Architectural Overview
At a high level, the system comprises three layers: the control plane, the tool registry, and the execution engine. The control plane is a state machine that receives a user prompt, decides which tool to invoke next, and updates its internal context based on the tool’s output. The tool registry is a declarative list of available actions—each with a name, description, and a callable implementation. The execution engine is responsible for serializing the request, handling retries, and capturing logs.
The control plane’s decision logic is typically driven by a language model that has been fine‑tuned or prompted to act as a policy engine. For example, given the current context, the model might output a JSON object like {"action": "search", "parameters": {"query": "latest climate policy"}}. The control plane parses this instruction, looks up the corresponding tool in the registry, and forwards the parameters. Once the tool returns, the control plane updates the context and loops back, allowing the model to refine its next action. This loop continues until a termination condition is met, such as a maximum number of turns or a confidence threshold.
Control Plane Components
The control plane is implemented as a lightweight class that holds the current state, a reference to the tool registry, and a safety module. The state includes the original user prompt, a history of actions and results, and any intermediate variables the model may wish to track. The safety module is a plug‑in that can inspect each proposed action before it is executed. It can enforce policies like disallowing certain keywords, limiting the number of external calls, or applying content filters to the tool’s output.
A key design decision is how to represent the control flow. We use a deterministic finite automaton (DFA) where each state corresponds to a specific phase of the reasoning loop: initialization, tool selection, execution, post‑processing, and termination. Transitions are triggered by the model’s output or by the execution engine’s response. This explicit structure makes it trivial to add new states—such as a validation step that checks the correctness of a tool’s result—without touching the core logic.
Tool Integration
Tools are the building blocks that give the agent external capabilities. In our example we include three tools: a web search API, a summarization model, and a simple arithmetic calculator. Each tool is wrapped in a Python function that accepts a dictionary of parameters and returns a dictionary of results. The wrapper handles authentication, error handling, and response parsing.
The control plane does not need to know the internals of each tool. It simply passes the parameters and receives the output. This abstraction allows developers to add new tools—such as a translation service or a database query executor—by registering them in the tool registry. The registry itself is a JSON file or a database table that maps tool names to their metadata and callables. Because the control plane treats all tools uniformly, the same safety module can be applied to every call.
Safety and Rule Enforcement
Safety is paramount when building autonomous agents that can interact with the internet or manipulate data. Our safety module implements a multi‑layered approach. First, a policy filter scans the proposed action for disallowed keywords or patterns. Second, a rate limiter ensures that no single tool is called more than a configured number of times per minute. Third, a content sanitizer runs on the tool’s output to strip or flag any potentially harmful text.
The policy filter is implemented as a simple rule engine that can be extended with machine‑learning classifiers if needed. For example, a model can be trained to detect phishing‑like queries and automatically block them. The rate limiter uses a token bucket algorithm, which is efficient and easy to tune. Finally, the content sanitizer can integrate with existing libraries such as OpenAI’s Moderation API or a custom regex‑based filter.
Reasoning Loop Mechanics
The reasoning loop is the heart of the agent. It starts with the user’s prompt and proceeds through a series of think‑act‑observe cycles. In the think phase, the language model receives the current context and outputs the next action. In the act phase, the control plane invokes the chosen tool. In the observe phase, the output is fed back into the context, and the loop continues.
To avoid infinite loops, the control plane tracks the number of turns and imposes a hard limit. It also monitors the model’s confidence scores; if the confidence falls below a threshold, the agent can request clarification from the user. This feedback loop is essential for maintaining user trust and preventing the agent from producing nonsensical or harmful responses.
Retrieval System Setup
Retrieval is a common requirement for knowledge‑intensive agents. In our tutorial we set up a lightweight vector store using FAISS. Documents are embedded with a sentence transformer model and indexed. The search tool queries the vector store and returns the top‑k most relevant passages. The summarization tool then condenses these passages into a concise answer.
The retrieval component is deliberately decoupled from the control plane. The search tool simply receives a query string and returns a list of passages. The control plane can decide whether to perform a second search, refine the query, or skip the retrieval step altogether based on the user’s request. This flexibility is crucial when dealing with noisy or ambiguous queries.
Modularity and Scalability
Because each component is isolated, scaling the system is straightforward. The control plane can run on a single CPU core for small workloads, but can also be distributed across multiple nodes for high‑throughput scenarios. Tools that are I/O bound—such as API calls—can be wrapped in asynchronous functions, allowing the control plane to issue multiple calls concurrently.
Modularity also simplifies maintenance. If a tool’s API changes, only its wrapper needs to be updated. If the safety policy evolves, the policy filter can be re‑trained or re‑configured without touching the rest of the codebase. This separation of concerns is a hallmark of robust software engineering and is especially valuable in regulated industries where auditability is required.
Practical Example Walkthrough
Let’s walk through a concrete example. A user asks, “What are the latest regulations on carbon credits in the EU?” The control plane receives the prompt and, via the language model, decides to search the web for “EU carbon credit regulations 2025”. The search tool queries the vector store and returns a handful of policy documents. The control plane then instructs the summarize tool to condense the key points. The summarization output is fed back into the context, and the agent finally produces a concise answer for the user.
During each step, the safety module checks that the search query does not contain disallowed content and that the summarization output does not exceed the token limit. If the agent detects that the policy documents are behind a paywall, it can either request the user to provide credentials or offer a high‑level summary based on publicly available excerpts.
Conclusion
By adopting a control‑plane architecture, developers can build agentic AI systems that are not only powerful but also safe, modular, and scalable. The clear separation between orchestration, tool execution, and safety enforcement turns a monolithic chatbot into a flexible platform that can evolve with new models, APIs, and regulatory requirements. The tutorial demonstrates that even a lightweight implementation can handle complex reasoning tasks, making it an excellent starting point for both researchers and practitioners.
The key takeaways are: 1) the control plane should be the single source of truth for decision making, 2) tools should be treated as black‑box services with well‑defined interfaces, 3) safety must be baked into the orchestration logic, and 4) modularity enables rapid iteration and scaling. With these principles in place, the next generation of AI assistants can deliver reliable, context‑aware, and trustworthy interactions at scale.
Call to Action
If you’re excited to experiment with agentic AI, start by cloning the repository linked in the tutorial and running the minimal example on your machine. Try adding a new tool—perhaps a weather API or a code‑generation service—and observe how the control plane adapts without any changes to the core logic. Share your results on GitHub or Twitter, and let the community help refine the safety policies and performance optimizations. Together, we can push the boundaries of what autonomous agents can achieve while keeping them safe and reliable for everyone.