When we talk about large language models, the conversation often drifts into a fog of anthropomorphism. We say the model “thinks,” “understands,” or “remembers.” While these metaphors are useful for casual conversation, they collapse under the weight of engineering rigor. To build robust systems, we need to strip away the magic and look at the machinery. We need a mental model that distinguishes between a static predictor, a recursive loop, and an autonomous actor.
For years, the industry has treated the Large Language Model (LLM) as a monolith. But as we push the boundaries of what these systems can do, the distinctions between a raw model, a recursive reasoning pattern, and a full agentic framework have become critical. Understanding these differences isn’t just academic; it is the difference between a system that hallucinates a fictional court case and one that retrieves the actual statute.
The Foundation: The LLM as a Stateless Function
At its core, a Large Language Model is a probabilistic engine. It is a massive, frozen matrix of weights—often billions of parameters—transforming a sequence of tokens into a probability distribution over the next possible token. When we interact with an LLM via an API, we are essentially calling a stateless function.
Consider the mathematical operation: $P(x_t | x_{t-1}, x_{t-2}, …, x_0)$. The model takes a context window (the prompt) and predicts the next token. Once the response is generated, the transaction is usually complete. The model does not inherently “know” what it just said unless that text is explicitly fed back into the context for the next turn.
This statelessness is both the LLM’s greatest strength and its most frustrating limitation. It is why a model can give you a perfect Python script in one turn and, in the next, forget the variable names it just invented if you don’t include them in the new prompt.
The “memory” of an LLM is an illusion created by the context window. It is not recall; it is re-feeding data into the function.
In a standard chat interface, the system maintains the illusion of memory by secretly concatenating the conversation history and sending the entire transcript back to the model with every new message. This is computationally expensive and token-heavy. It is not true memory; it is a brute-force repetition of state.
From a control flow perspective, the LLM is linear. It receives input and produces output. There is no internal loop, no self-correction mechanism, and no agency. If the prompt is ambiguous, the model makes a “best guess” based on its training distribution. It does not stop to ask for clarification unless the prompt explicitly instructs it to simulate that behavior.
Recursive Logic: The Emergence of Reasoning
If the raw LLM is a single step, Recurrent Language Models (RLM)—or more accurately, recursive prompting patterns—introduce a loop. This is where the concept of “Chain of Thought” (CoT) and reasoning traces enters the picture. The model is asked not just to answer, but to show its work.
Recursive prompting is a software engineering pattern applied to a probabilistic engine. We treat the LLM as a subroutine that we call repeatedly, refining the output with each iteration.
Consider the problem of solving a complex logic puzzle. A zero-shot prompt to a raw LLM might result in a 60% success rate. By enforcing a recursive structure—where the model first generates a plan, then critiques that plan, and finally executes it—we can push that accuracy significantly higher.
The Control Flow of Recursion
In an RLM-style system, the control flow is managed externally by the programmer (or a meta-prompt). It looks roughly like this:
- Generation: The LLM produces a draft response.
- Evaluation: The LLM (or a separate instance) evaluates the draft against constraints.
- Refinement: The LLM revises the draft based on the evaluation.
This is the mechanism behind frameworks like Tree of Thoughts (ToT). Instead of a single linear path of text generation, the system explores multiple branches of reasoning, evaluates them, and prunes the ones that lead to dead ends.
It is crucial to distinguish this from the model “thinking” in a human sense. The model is not introspecting. It is generating text that looks like introspection. When we prompt an LLM with “Let’s think step by step,” we are not unlocking a hidden cognitive process; we are steering the token generation toward a format that correlates with higher accuracy in the training data.
Reliability in recursive systems stems from redundancy. By generating multiple paths and verifying them, we smooth out the stochastic noise inherent in the model’s sampling process. However, this introduces latency and cost. Every recursive step is another API call or another forward pass through the neural network.
The Risk of Infinite Loops
Recursive systems are brittle. Without careful termination conditions, they can hallucinate a loop. I have personally调试ed systems where the model’s “critic” phase was too lenient, approving the “generator’s” output regardless of quality, leading to an infinite loop of refinement that consumed thousands of tokens without converging on a solution.
This highlights a key difference between RLM patterns and raw LLMs: the RLM pattern introduces state management complexity. You are no longer dealing with a single function call but a dynamic process that requires timeouts, iteration limits, and validation gates.
Agentic Systems: The Pursuit of Autonomy
If the LLM is the brain and the RLM pattern is the thought process, the Agentic System is the body interacting with the world. This is the most significant evolution in the stack, moving from text-in, text-out to perception, reasoning, and action.
An agent is not defined by its ability to generate text, but by its ability to manipulate its environment. In technical terms, an agent is a system that combines an LLM with tools (functions/APIs) and memory (persistent state) to achieve a goal.
Tool Use as Function Calling
Modern LLMs (like GPT-4 or Claude) support native function calling. This is the bridge between probabilistic text and deterministic code. When an agent decides to use a tool, it is effectively shifting from a generative mode to a structured output mode. It outputs a JSON object specifying which function to call and with what arguments.
For example, an agent asked to “Check the weather in London” does not simply predict the words “It is sunny.” It predicts a structured call:
{
"tool": "get_weather",
"arguments": {"location": "London"}
}
The system then executes this function, retrieves the real-world data, and feeds it back into the LLM to generate the final natural language response.
This architecture fundamentally changes the reliability equation. When the LLM writes code, it might make syntax errors. But when the LLM calls a calculator tool, the arithmetic is exact. The agent offloads deterministic tasks to external systems, reserving the LLM’s flexibility for the ambiguous parts of the problem.
Memory: Beyond the Context Window
Agents require memory that persists beyond the immediate context window. This is usually implemented in two layers:
- Short-term Memory (Working Memory): The current conversation context or the immediate scratchpad.
- Long-term Memory (Episodic/Semantic): A vector database (RAG) or a traditional database storing past interactions and facts.
Retrieval-Augmented Generation (RAG) is often categorized separately, but in an agentic context, it is simply a specific type of memory tool. The agent decides when to retrieve information, much like a human deciding when to look up a fact in a notebook.
The complexity here lies in the retrieval mechanism. A naive agent might retrieve documents based on simple semantic similarity. A sophisticated agent might decompose the query, retrieve documents for each sub-query, and then synthesize the results. This is where the “Agentic” pattern diverges sharply from simple RLM loops: the agent maintains a dynamic state of the world that changes based on external inputs.
Planning and Decomposition
High-level agentic frameworks often employ a “planner” component. The planner takes a high-level goal (e.g., “Research the impact of AI on semiconductor stocks”) and breaks it down into sub-tasks.
- Search for recent news on AI chips.
- Summarize key findings.
- Check stock performance for NVIDIA and AMD.
- Generate a report.
This planning is often recursive. The agent generates a plan, executes a step, observes the result, and updates the plan. This feedback loop is what gives agentic systems their adaptability. They are not just following a static script; they are reacting to the output of their actions.
Comparative Analysis: Control, Memory, and Reliability
To unify these concepts, we can map them across three critical dimensions: Control Flow, Memory Architecture, and Reliability Mechanisms.
1. Control Flow
LLM (Stateless): Linear execution. Input $\rightarrow$ Output. The flow is strictly unidirectional within a single call. Complexity is limited by the prompt engineering.
RLM (Recursive): Cyclical execution. The system iterates over a problem space. Control is managed by a meta-loop (often external code). The flow is $Input \rightarrow Output \rightarrow Evaluation \rightarrow Refined Output$.
Agentic (Autonomous): State-machine execution. The flow is non-deterministic and depends on external conditions. $State \rightarrow Action \rightarrow Observation \rightarrow New State$. The agent chooses the next step based on the current context and available tools.
2. Memory
LLM: Ephemeral. Memory is the context window. Once the window is cleared, the history is lost. No learning occurs between interactions.
RLM: Context-bound. Memory exists only as long as the recursive loop persists. It can reference previous steps in the chain of thought, but this consumes valuable token space. It is essentially working memory.
Agentic: Persistent. Agents utilize external vector stores or databases. They can recall information from previous sessions (long-term memory) and maintain a scratchpad of actions (short-term memory). This allows for continuity over time.
3. Reliability
LLM: Low reliability for complex tasks. Susceptible to hallucination. Reliability is purely a function of the model’s training data distribution. No self-correction.
RLM: Medium reliability. Recursive self-criticism and verification loops reduce hallucination rates. However, the system is still confined to the knowledge within the model weights. If the model doesn’t know a fact, reasoning about it won’t make it appear.
Agentic: High potential reliability. By offloading factual retrieval and calculation to external tools, the agent grounds its generation in reality. However, reliability is now dependent on the orchestration logic. A bug in the tool-calling logic or a failure in the retrieval step can break the entire chain.
The Unifying Mental Model
How do we unify these into a single mental model? We can view them as layers of abstraction over a computational engine.
Imagine a stack:
- The Base Layer (LLM): A universal text processor. It accepts text and predicts text. It is the raw compute power.
- The Logic Layer (RLM): A software layer that wraps the LLM. It introduces loops, branching, and verification. It turns the text processor into a reasoning engine.
- The Interaction Layer (Agent): The outermost shell. It connects the reasoning engine to the world. It manages goals, persistence, and external APIs.
In this view, an Agent is not a replacement for an LLM; it is an LLM augmented with logic and interfaces. A recursive system is an Agent that has temporarily turned its focus inward to solve a specific problem before turning outward to act.
This model explains why “agents” are so hard to build reliably. You are not just debugging code; you are debugging the interaction between a deterministic control flow and a probabilistic reasoning engine. When an agent fails, it could be because:
- The LLM hallucinated a tool argument (Base Layer failure).
- The recursive loop didn’t converge on a valid plan (Logic Layer failure).
- The external API returned an unexpected error that the agent didn’t know how to handle (Interaction Layer failure).
Code as the Great Unifier
As developers, we often try to force these paradigms into rigid code structures. We define classes for Agents and functions for Tools. This is a helpful abstraction, but we must remember the underlying substrate.
Consider a Python implementation of an agent loop:
def agent_loop(goal):
memory = VectorStore()
while not goal_achieved:
context = memory.retrieve(goal)
# This is the LLM + RLM part
reasoning_trace = llm.generate(
prompt=f"Plan how to achieve: {goal} using context: {context}"
)
# Parsing the structured output
action = parse_action(reasoning_trace)
if action.type == "TOOL_CALL":
result = execute_tool(action)
memory.store(result)
elif action.type == "FINISH":
return reasoning_trace
This pseudocode illustrates the unification. The llm.generate call represents the LLM. The while loop and the parsing logic represent the RLM/Reasoning layer. The execute_tool and memory represent the Agentic layer.
The “intelligence” of the system is not located in any single line. It emerges from the interplay of these components.
Reliability and the Challenge of State
One of the most profound challenges in unifying these models is managing state. In traditional software engineering, state is explicit. Variables hold values; databases hold records. In LLM systems, state is implicit and often textual.
When we move from a raw LLM to an Agent, we are moving from a stateless function to a stateful process. This introduces the classic distributed systems problems: consistency, availability, and partition tolerance.
For example, if an agent is writing a report and retrieves a document, then updates its memory, but the LLM’s context window is full, which version of the state is “true”? The vector database might have the new information, but if the LLM cannot “see” it in the context window (because it’s too long), the agent effectively forgets its recent discovery.
This is why “long-term memory” in agents is often a misnomer. It is more accurate to call it “retrievable storage.” The agent does not remember in the biological sense; it retrieves and re-contextualizes information on demand.
The Hallucination Penalty
Reliability metrics differ across the spectrum. For a raw LLM, we measure perplexity or accuracy on benchmarks. For an RLM system, we measure the reduction in error rate through self-correction.
For an agent, reliability is measured by task completion. Did the agent successfully book the flight? Did it write and execute the code without runtime errors?
Agents face a unique failure mode: compounding errors. In a recursive loop, if the model makes a small error in step 1, step 2 might correct it. In an agent loop, if the model makes an error in step 1 (e.g., calls the wrong API), that action might change the state of the world in an irreversible way. For example, an agent with access to a “delete file” tool could permanently delete the wrong file based on a hallucination.
This necessitates a “human-in-the-loop” design for high-stakes agents. We must build guardrails—software constraints that prevent the agent from executing destructive actions without confirmation.
The Spectrum of Intelligence
It is tempting to view LLMs, RLMs, and Agents as distinct categories. A more accurate view is a spectrum of capability.
Static Prediction (LLM): The model is a mirror reflecting its training data. It has no agency, only capability.
Deliberative Reasoning (RLM): The model is guided to reflect on its output. It simulates a thought process, improving the quality of its predictions.
Interactive Agency (Agent): The model is coupled with actuators. It influences its environment and receives feedback, closing the loop between perception and action.
We are currently in a phase where these boundaries are blurring. New techniques like “implicit reasoning” (where the model reasons internally without emitting tokens) and “tool use” are being integrated directly into base models. The line between a “model” and an “agent” is becoming a configuration setting rather than a fundamental architectural difference.
However, the engineering principles remain distinct. Building a high-performance LLM requires optimizing matrix multiplications. Building a reliable recursive system requires careful prompt engineering and validation logic. Building a robust agent requires distributed systems design, error handling, and state management.
Practical Implications for Developers
For the engineer sitting down to build a system today, this mental model dictates the architecture.
If your problem is purely generative—summarizing text, translating languages, or generating creative writing—you are in the LLM domain. Optimize for the quality of the prompt and the inference parameters (temperature, top_p). Do not over-engineer a complex loop.
If your problem requires reasoning—solving math problems, writing complex code, or planning a sequence of steps—you need RLM patterns. Implement Chain of Thought prompting. Consider running multiple generations and selecting the best one (majority voting). Add verification steps.
If your problem requires interaction with the world—querying a database, browsing the web, or controlling a software interface—you need an Agent. Focus on defining clear tools with strict schemas. Implement robust error handling and memory management. Design your system to be resilient to the non-determinism of the LLM.
One common mistake is trying to solve an agent-level problem with an LLM-level approach. For instance, asking a raw LLM to “tell me the current stock price of Apple” will likely result in a hallucination based on training data. The correct approach is to recognize this as an agent problem: the LLM must be given a tool to fetch the price.
Conversely, using a heavy agentic framework to summarize a simple paragraph is overkill. It adds latency and points of failure where none are needed.
The Future of the Stack
As we look forward, the integration of these layers will deepen. We are seeing the rise of “multi-modal agents” that can see images and hear audio, not just read text. The principles of control flow, memory, and reliability remain the same, but the inputs and outputs expand.
The concept of “RLM” is evolving into “System 2 Thinking” in AI. Researchers are exploring ways to make the model slow down and think before answering, essentially embedding the recursive loop inside the model’s generation process rather than orchestrating it externally.
However, the separation of concerns remains vital. The probabilistic nature of the underlying transformer architecture means that we will always need external validation and deterministic tooling to ensure reliability. The “brain” of the LLM is powerful but erratic; the “body” of the agent must be built with the rigor of traditional software engineering.
Ultimately, the unifying mental model is one of composition. We are not building monoliths; we are composing systems. We take the predictive power of the LLM, wrap it in the logic of RLM patterns, and encase it in the interactive shell of an Agent.
When we understand these components not as competing technologies but as layers of a single stack, we can build systems that are greater than the sum of their parts. We move beyond the hype of “artificial general intelligence” and into the practical reality of building tools that extend human capability, one reliable, well-architected layer at a time.
The journey from a raw LLM to a fully autonomous agent is a journey from static probability to dynamic interaction. It is a shift from asking “What comes next in this text?” to “What should I do next in the world?” Mastering this shift is the defining challenge for the next generation of AI developers.
