When we talk about building intelligent systems, the terminology often drifts toward buzzwords that obscure the underlying mechanics. We hear “agent” and “LLM” used interchangeably, as if simply wrapping a language model in a loop automatically imbues it with agency. But the architectural differences between a recursive language model (RLM) pattern and a true agent loop are profound, and they dictate the reliability, determinism, and failure modes of your system.
At the heart of this distinction lies control flow. It’s not merely about how the system decides what to do next; it’s about who owns the state, how memory is structured, and what guarantees the system can offer regarding termination and correctness. To understand this, we have to move beyond the marketing slides and look at the graph of execution.
The Illusion of the Loop
Most developers start with a simple pattern: the recursive call. You feed a prompt to an LLM, it generates an answer, and if that answer isn’t satisfactory, you feed the original prompt and the new answer back into the model, asking it to refine or iterate. This is the RLM pattern. It mimics recursion because the function (the model call) calls itself with modified parameters until a base case (a satisfactory answer) is reached.
There is an immediate seduction here. It feels like the model is “thinking” deeper. The output gets longer, more detailed, seemingly more reasoned. But this is an illusion of depth, not an increase in structural integrity. In a pure RLM recursion, the control flow is linear and brittle. The model is the sole arbiter of the state, and the state is nothing more than the growing context window.
“The context window is not a database. It is a noisy, lossy transcript of a conversation that you are forcing a probabilistic engine to complete.”
When you rely on recursion, you are betting that the model’s next token prediction will respect the constraints of the previous turns. But as the context grows, the signal-to-noise ratio degrades. The model begins to hallucinate patterns in the noise, or worse, it forgets the original system instructions buried at the top of the prompt. The control flow is entirely dependent on the model’s ability to self-regulate, which is, statistically speaking, unreliable.
The Determinism Gap
In a recursive LLM setup, the termination condition is fuzzy. You might set a maximum depth (e.g., “recurse 5 times”), but you rarely have a mathematical proof that the answer improves with each step. In fact, it often oscillates. The model might correct a mistake in iteration 3 only to reintroduce it in iteration 4 because the statistical likelihood of that error state is high within the specific context constellation you’ve built.
This creates a “determinism gap.” You cannot guarantee that the recursion will converge on a solution. You can only hope that the probability of convergence increases with compute. This is expensive and unpredictable.
Agents: Explicit State Management
Contrast this with the Agent pattern. An agent is not defined by the presence of an LLM, but by the presence of a controller that manages state and executes actions. In a robust agent architecture, the LLM is merely a component—a reasoning engine or a function caller—not the runtime itself.
The key differentiator is the separation of the Reasoning Loop from the Execution Loop.
The Planner-Executor Model
True agents typically employ a planner. The planner takes the current goal and the current state and produces a plan—a sequence of steps. This plan is stored in a structured data format (JSON, a knowledge graph, or a task queue), not in natural language text. The executor then picks a step, calls a tool or an API, observes the result, updates the state, and feeds that update back to the planner.
Notice the difference in control flow:
- RLM: Input (Context) → Output (Text) → Input (Context + Text) …
- Agent: State → Planner (LLM) → Action → Observation → State Update → (Loop)
The agent loop has a distinct advantage: externalized memory. The state is not lost in the token stream of a previous turn. It lives in a vector database, a SQL table, or a graph node. This allows the system to “forget” irrelevant context and “recall” specific facts without bloating the prompt.
Guarantees and Hallucination Mitigation
Because the agent loop relies on explicit state transitions, we can implement hard guarantees. We can validate the output of the LLM before acting on it. For example, if the planner outputs a tool call, we can schema-validate that call. If it fails validation, we don’t execute it; we return an error to the planner and ask it to try again.
In an RLM, a hallucinated fact is simply text. It blends into the output, indistinguishable from truth unless you have an external verification step. In an agent, a hallucinated action (e.g., trying to call a tool that doesn’t exist) is caught by the runtime environment. The agent cannot crash the system because the environment (the code hosting the agent) enforces boundaries.
This is the concept of Defensive Control Flow. The agent assumes the LLM will make mistakes and structures the loop to handle those mistakes gracefully. The RLM assumes the LLM will eventually get it right, which is a dangerous assumption in production systems.
Failure Modes: Divergence vs. Dead Ends
Understanding how these systems fail is critical for engineering robust applications. The failure modes of RLMs and agents are fundamentally different.
RLM Failure: Infinite Loops and Context Bloat
Recursive LLMs are prone to divergence. Without a strict stopping heuristic, they can enter infinite loops where they rephrase the same sentence endlessly. I’ve seen systems where a model gets stuck in a “politeness loop,” apologizing and re-offering a solution that was already rejected.
Furthermore, RLMs suffer from context saturation. As the recursion depth increases, the available tokens for new reasoning decrease. The model eventually hits the context limit, at which point the oldest information is truncated. If that truncated information was a critical system instruction, the model’s behavior becomes chaotic. The failure is silent and insidious; the system appears to work but has lost its grounding.
Agent Failure: Dead Ends and Recovery
Agents fail differently. They hit dead ends. An agent might execute a tool that returns an empty result or an error code. The control flow then branches. A well-designed agent doesn’t crash; it analyzes the error and adjusts its plan.
However, agents introduce complexity in the control graph. Because the agent can choose from multiple tools and paths, the number of possible execution paths grows combinatorially. This leads to the “state explosion” problem. An agent might pursue a sub-goal that is logically sound but practically irrelevant, wasting tokens and API calls.
The failure mode here is not infinite recursion but wasted effort. An agent might loop through a “research” phase indefinitely, gathering more data without ever transitioning to an “execution” phase. This requires a meta-controller—a supervisor agent that monitors the sub-agent and enforces timeouts or budget constraints.
The Recursion Depth Trade-off
Let’s look at the mathematics of the context window. In an RLM, the effective context length $L_{eff}$ decreases with every iteration $i$. If the average response length is $r$, and the maximum context is $C$, the available space for reasoning in step $i$ is roughly $C – i \times r$.
Eventually, $i \times r$ approaches $C$. At this point, the model begins to “forget” the initial constraints. This is a hard limit imposed by the architecture of transformers. No amount of prompt engineering can fix the physics of token allocation.
Agents bypass this by design. The “reasoning” context in an agent is typically much smaller. The LLM is asked to generate a structured action, not a long-form essay. The history of the conversation might be summarized or stored in a vector database, and only relevant snippets are retrieved when needed. This is Retrieval-Augmented Generation (RAG) applied to the agent’s own history.
By decoupling the reasoning context from the execution history, agents can theoretically run forever (or at least until the API budget is exhausted) without degrading performance due to context length. The state grows, but the active context remains focused.
Tool Use and The Sandbox
When we introduce tools (APIs, code interpreters, database queries), the distinction becomes even sharper.
In an RLM setup, “tool use” is often simulated via prompt engineering. You tell the model: “If you need to calculate something, write ‘CALCULATE:
Agents use structured output (JSON schemas) enforced by the model provider (e.g., OpenAI’s function calling or JSON mode). The control flow is:
- Agent requests a structured response.
- Model returns valid JSON matching the schema.
- Code parses the JSON and executes the function.
The validation happens before execution. This is a critical safety mechanism. In the RLM text-parsing approach, execution happens blindly based on regex matching, which is prone to injection attacks and logic errors.
Implementation Patterns: Code vs. Prompts
As developers, we must choose where to put our logic. In an RLM pattern, logic is embedded in the prompt. We are essentially programming in natural language. This is flexible but hard to debug. Changing a single word in a prompt can drastically alter the control flow.
In an Agent pattern, logic is embedded in code. The prompts are instructions, but the flow is dictated by `if/else` statements, `for` loops, and state machines. This is familiar territory for engineers. We can write unit tests for the state transitions. We can mock the LLM’s reasoning output to test if the execution logic works.
Consider a scenario where we want to build a system that books flights.
RLM Approach: “You are a travel agent. The user wants a flight to Paris. Search for flights, compare prices, and book the best one.” The model tries to do everything in one go. If the API call fails, the model has to interpret the error message and decide what to do next, all within the text stream. It’s messy.
Agent Approach: We define states: `NEED_DESTINATION`, `SEARCH_FLIGHTS`, `COMPARE_PRICES`, `BOOK`. We define tools: `search_flights(origin, dest)`, `book_flight(flight_id)`. The agent code looks like this:
while not done:
if state == SEARCH_FLIGHTS:
tool_call = llm.decide_tool(search_tools)
result = execute(tool_call)
state = COMPARE_PRICES if result else ERROR_HANDLER
The agent approach is verbose but robust. It separates the “thinking” from the “doing.”
The Latency Factor
There is a performance trade-off. RLMs can be faster for simple tasks because they minimize the number of round-trips to the API. If the model can solve the problem in one long generation, it’s faster than an agent that makes five separate API calls to plan and execute.
However, for complex, multi-step tasks, agents often win on total latency. Why? Because they can parallelize. An agent can issue multiple tool calls simultaneously (or in rapid succession) if the tasks are independent. An RLM is inherently sequential; it must generate the text for step 1 before it can “see” the result of step 1.
Moreover, agents can fail fast. If a tool returns an error immediately, the agent can pivot. An RLM might spend 200 tokens reasoning about a step that was impossible from the start, wasting time and money.
Memory Architectures: Context vs. Database
We need to talk about memory because it is the backbone of long-term coherence.
In an RLM, memory is the context window. It is associative and ephemeral. The model remembers things that are statistically prominent in the recent tokens. It does not have a concept of “importance” unless explicitly prompted to summarize.
Agents can implement sophisticated memory systems. We can use:
- Episodic Memory: Storing past interactions in a vector store and retrieving them based on semantic similarity.
- Procedural Memory: Hard-coded rules and workflows that don’t require LLM reasoning.
- Working Memory: The current state variables.
Imagine an agent that has been running for a week. It has accumulated thousands of interactions. In an RLM, you cannot feed all those interactions into the context. You have to summarize them, losing nuance. In an agent, you can query the vector database: “What did the user say about their dietary preferences last Tuesday?” and inject that specific, high-fidelity snippet into the context.
This ability to retrieve precise facts rather than relying on probabilistic recall is what separates a toy demo from a production-grade AI system.
The Supervisor Pattern
As we scale, we often move from single agents to multi-agent systems. This is where control flow becomes a graph.
A common pattern is the Supervisor. A central LLM acts as a router. It receives the user request and decides which specialized agent should handle it (e.g., a “Coder” agent, a “Researcher” agent, a “Critique” agent). The supervisor manages the conversation flow between these agents.
This is distinct from a recursive RLM. In an RLM, the model is monolithic—it tries to be everything. In a multi-agent system, the control flow is distributed. The supervisor doesn’t know how to code; it just knows how to delegate. The coder agent doesn’t know how to search the web; it just knows how to write code.
This separation of concerns is classic software engineering, applied to AI. It limits the blast radius of errors. If the coder agent hallucinates a library, the critique agent (or a linter tool) can catch it before it reaches the user.
Practical Guidance: When to Use Which?
If you are building a system today, how do you decide?
Use RLM (Recursive) patterns when:
- The task is creative or open-ended (e.g., writing a story, brainstorming).
- The number of steps is small (1-3 iterations).
- Latency is critical, and you want to minimize network round-trips.
- You don’t need external data or tools, just text manipulation.
Use Agent patterns when:
- The task requires deterministic steps or tool usage (APIs, databases).
- Reliability and error handling are paramount.
- The context history is long or requires precise retrieval.
- You need to audit the decision-making process (the structured logs of an agent are much easier to debug than a wall of chat text).
The Hybrid Approach
The most advanced systems today are hybrids. They use an agent framework to handle the macro control flow (state management, tool execution) but allow the LLM to use “internal recursion” for micro-tasks.
For example, an agent might be in a “Coding” state. Inside that state, it might use a recursive loop to iterate on a piece of code: “Generate code → Run Linter → Fix errors → Run Linter.” This is a localized RLM pattern embedded within a larger Agent state machine.
This allows us to get the best of both worlds: the flexibility of recursive reasoning for small, contained problems, and the robustness of explicit state management for the overall application logic.
Looking Ahead: The Evolution of Control
As model capabilities increase, the line between RLM and Agent may blur. We are seeing models with larger context windows (200k+ tokens) that can handle more recursion without losing the plot. We are also seeing models with built-in tool-use capabilities that feel more like agents even when used recursively.
However, the fundamental constraint of who owns the state will remain. If the state lives only in the tokens, it is fragile. If the state lives in a database or a graph, it is resilient.
For the foreseeable future, engineering robust AI systems will require us to treat the LLM as a component, not the container. We must build the container—the agent loop—ourselves. We must define the states, the transitions, the error handlers, and the memory stores.
Writing this code is not trivial. It requires a shift in mindset from “writing prompts” to “designing systems.” But it is the only path toward applications that don’t just sound smart but actually work.
The control flow you choose determines the destiny of your application. Choose the loop that offers you the most control, the most observability, and the most resilience against the inherent randomness of the models you are driving.
When you are debugging a production issue at 2 AM, you will thank yourself for having a structured log of state transitions rather than a cryptic chat history that you have to manually parse to understand where the model went off the rails.
And remember: the goal isn’t to mimic human conversation; it’s to solve problems. Sometimes that requires a chat. Often, it requires a well-oiled machine.
