The concept of an “agent” has captured the imagination of the software engineering community, particularly in the wake of large language models (LLMs). We envision autonomous entities capable of taking a vague instruction and executing a complex series of steps to achieve a goal. However, anyone who has spent time building these systems knows a frustrating reality: left to their own devices, agents tend to drift. They hallucinate, they loop, they lose the thread of the conversation, and they fail to complete tasks that a human programmer could finish in minutes.
This failure mode is not a bug in the model’s weights; it is a fundamental lack of architectural grounding. An agent without explicit structure is a probabilistic text generator wandering through a state space without a map. To understand why they fail—and how to fix it—we must look beyond the hype and examine the intersection of Reinforcement Learning (RLM), ontologies, and the rigid scaffolding required to turn a model into a reliable tool.
The Illusion of Autonomy
There is a pervasive misunderstanding in the current discourse that intelligence emerges automatically from scale. We assume that because a model can predict the next token in a sequence describing a plan, it can also execute that plan. This is a category error. Planning is a static cognitive act; execution is a dynamic interaction with an environment that changes state.
When we give an LLM a goal like “research the best cloud provider for a startup,” it generates a stream of text that looks like reasoning. It might list criteria, compare prices, and offer a recommendation. But this is merely a simulation of research. The model is not querying APIs, it is not scraping real-time pricing data, and it is not validating its assumptions against a database. It is mimicking the structure of a researched answer based on patterns in its training data.
Without external structure to ground these steps—APIs to call, databases to query, tools to use—the agent is trapped in its own internal monologue. It drifts because it has no friction. In physics, friction is a force that resists motion, often seen as a hindrance, but in dynamics, it is essential for control. Without the friction of external constraints and state verification, the agent’s internal reasoning slips into hallucination. It fills in gaps in knowledge with plausible-sounding fiction because its objective function (predicting the next token) rewards coherence over factual accuracy.
Statelessness and the Problem of Memory
One of the most glaring structural deficits in current agent architectures is the lack of persistent, episodic memory. A standard LLM interaction is stateless. The model remembers only what is contained in the current context window. As the conversation or task lengthens, the context fills up, and the earliest parts of the interaction are compressed or dropped entirely.
Consider a debugging session. A human engineer might spend hours narrowing down an issue, keeping a mental map of what has been tested and ruled out. An agent operating on a rolling context window eventually forgets why it made a specific decision three turns ago. It might circle back to a solution it discarded earlier, or it might lose the specific error message that initiated the investigation.
This is where the distinction between working memory and long-term memory becomes critical. Human cognition relies on an externalized structure (notes, code comments, diagrams) to offload context. Agents require the same. Without a structured memory system—whether a vector database, a knowledge graph, or a simple structured log—the agent is perpetually starting over. It cannot build upon previous successes or learn from immediate failures because it has no mechanism to encode those experiences into a format retrievable for future steps.
The drift occurs because the agent lacks a “self” that persists across time. It is a sequence of isolated instances, each trying to guess the narrative of the previous one. This fragmentation prevents the accumulation of knowledge necessary for complex, multi-step tasks.
The Absence of Explicit Goals and Reward Shaping
Reinforcement Learning (RL) provides a rigorous framework for understanding how agents learn to act. In RL, an agent interacts with an environment, receives a state, takes an action, and receives a reward. The goal is to maximize the cumulative reward. When we talk about RLM (Reinforcement Learning from Human Feedback or Machine Feedback), we are discussing the process of shaping behavior through signals.
However, in the current LLM-agent paradigm, the “reward” is often implicit and ill-defined. We ask a model to “be helpful.” This is a vague, high-level objective. Without a clear, quantifiable reward signal at every step of the process, the agent optimizes for the wrong things.
For example, if an agent is tasked with writing code, and the reward is simply “does the code compile?”, the agent might write incredibly convoluted, unmaintainable code that technically compiles. If the reward is “does the code pass the unit tests?”, the agent might hardcode outputs to pass specific test cases without solving the general problem.
Structure is the mechanism by which we define these rewards. It forces the agent to move from a fuzzy objective to a series of verifiable sub-goals. Without this hierarchy, the agent drifts toward local optima—solutions that look good in the immediate context but fail to satisfy the overarching intent. It is the difference between giving a robot a destination on a map versus telling it to “go somewhere interesting.” The latter will result in aimless wandering.
Ontologies: The Scaffolding of Reality
If memory provides continuity and rewards provide direction, ontologies provide the map. An ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that exist in a domain. In software terms, it is the schema of reality.
LLMs operate on natural language, which is inherently ambiguous. The word “bank” can mean a financial institution or the side of a river. In a structured system, an ontology resolves this ambiguity by defining entities and their relationships. A “User” has a “BankAccount” which belongs to a “FinancialInstitution.” The “River” has a “Bank” which is a “GeographicalFeature.”
When an agent lacks an explicit ontology, it relies on the probabilistic associations within its weights to determine relationships. This leads to “concept drift.” The agent might start a task assuming a specific definition of a term, but as the context shifts, its internal representation of that term shifts slightly. Over long interactions, these slight shifts compound, leading the agent to operate on a model of the world that no longer matches the actual problem domain.
Structured ontologies act as a constraint on the agent’s reasoning space. They limit the possible hallucinations by strictly defining what entities exist and how they relate. In knowledge graphs, for instance, an edge between two nodes represents a hard semantic relationship. An agent querying a knowledge graph is grounded in a verified structure; an agent querying its own context window is grounded in statistical noise.
Integrating an ontology turns the agent from a text generator into a reasoning engine. It forces the model to map its outputs to specific nodes and edges in a graph, ensuring that every claim it makes is tethered to a structural element of the domain.
The Role of RLM in Enforcing Structure
Reinforcement Learning from Machine Feedback (RLM) is often used to align models with human preferences, but it is equally powerful for enforcing structural adherence. We can train agents not just to produce “good” outputs, but to produce outputs that conform to a specific schema or API contract.
Consider an agent designed to interact with a database. If the agent is trained purely on next-token prediction using natural language, it might generate SQL queries that are syntactically incorrect or semantically unsafe. However, if we apply RLM with a reward function that penalizes syntax errors and rewards queries that return the expected result set, the agent learns to navigate the strict structure of SQL.
This process highlights a critical insight: structure is not just a container for the agent; it is a teacher. By defining the boundaries of valid actions (e.g., available tools, API endpoints, data schemas), we create a curriculum for the agent. The agent learns that certain paths lead to high rewards (success) and others lead to penalties (errors).
Without this, the agent is like a child in a room full of buttons with no instructions. It will press them randomly, and occasionally it will open the door, but it has no understanding of why. RLM provides the feedback loop that turns random exploration into deliberate action. It bridges the gap between the model’s latent knowledge and the explicit requirements of the task.
Tool Use as Structural Anchoring
One of the most effective ways to prevent agent drift is to force it to use external tools. This is the core premise behind frameworks like ReAct (Reasoning + Acting) and function calling interfaces in modern LLM APIs.
When an agent is restricted to text generation, it is free to invent facts. When it is required to call a function like get_current_weather(location), it is forced to accept a reality dictated by the external system. The tool call acts as a checkpoint. The agent cannot proceed to the next step of reasoning until it has received a valid response from the tool.
This creates a rigid skeleton for the agent’s thought process. The reasoning might be fluid, but the actions are discrete and verifiable. If an agent attempts to call a tool with invalid parameters, the system rejects it. This rejection is a strong signal—a negative reward—that forces the agent to re-evaluate its internal state.
Without these structural anchors, agents tend to “gaslight” themselves. They generate a hypothetical tool response and then reason based on that fabricated data. This is a classic failure mode in unstructured agents. By mandating that all external data acquisition happens through defined tool interfaces, we eliminate the possibility of self-deception.
Recursive Planning and Hierarchical Decomposition
Complex tasks cannot be solved in a single linear pass. They require hierarchical decomposition. A high-level goal must be broken down into sub-tasks, which are further broken down into atomic actions. An agent without a structure for planning tends to flatten this hierarchy. It tries to solve everything at once, leading to cognitive overload (context window limits) and logical inconsistencies.
Structured agents employ recursive planning. They generate a plan, execute one step, observe the result, and then update the plan. This is similar to the Model-View-Controller (MVC) pattern in software architecture, but applied to cognition.
The “Model” is the agent’s internal representation of the world state.
The “View” is the observation from the environment (tool outputs).
The “Controller” is the policy that updates the model and decides the next action.
Without an explicit representation of the world state (the Model), the agent has nothing to update. It views every observation in isolation. This leads to a lack of long-term coherence. For example, if an agent deletes a file in step 1, it must remember that the file is gone in step 10. If it lacks a structured state representation, it might attempt to read that file in step 10, fail, and be confused by the error because its internal “memory” of the file system is outdated.
Recursive planning requires a structured format for the plan itself—often JSON or XML—that can be parsed, modified, and validated. This allows the agent to “think” about its own thinking, a metacognitive ability that is impossible without a structural container for its thoughts.
The Dangers of Over-Structuring
While structure is essential to prevent drift, it is possible to over-constrain an agent. If the ontology is too rigid or the reward function too specific, the agent loses its ability to generalize or handle edge cases. This is the exploration-exploitation trade-off.
An agent that is strictly bound to a predefined workflow will fail gracefully when it encounters a scenario outside that workflow. It will hit a dead end and stop. Conversely, an unstructured agent will hallucinate a solution, which is often worse.
The art of agent design lies in finding the right level of abstraction for the structure. The structure should define the boundaries of the playing field but leave enough room for the model’s reasoning capabilities to operate. For instance, defining a set of available tools is good structure; defining the exact order in which tools must be used is often too restrictive.
We must also consider the computational cost of structure. Retrieving information from a vector database, parsing a complex ontology, or validating a plan against a schema adds latency and tokens to the context window. In high-throughput systems, these costs compound. Therefore, the structure must be efficient. It should filter information aggressively, providing the agent only with the relevant context needed for the immediate decision.
Case Study: The Unstructured Research Agent
Let us imagine a research agent tasked with summarizing recent developments in quantum computing.
The Unstructured Approach: We give the agent the prompt: “Find recent papers on quantum computing and summarize them.” The agent starts by generating a list of search queries. It “knows” that papers exist on arXiv, but without a tool to actually search arXiv, it relies on its training data. It might list papers from 2020, 2021, and hallucinate papers from 2023 that sound plausible. It summarizes these. The result is a mix of outdated facts and fiction. The agent drifted because it had no mechanism to verify the existence or content of the papers.
The Structured Approach:
1. Ontology: We define an ontology for academic research: Papers have Titles, Authors, Abstracts, and Publication Dates. The domain is “Quantum Computing.”
2. Tools: We provide a tool search_arxiv(query, date_range).
3. Memory: We provide a vector store where the agent stores the raw text of the papers it retrieves.
4. RLM/Reward: The agent is rewarded for citing papers that exist (verified via the tool) and for generating summaries that contain specific technical details found in the abstracts.
The structured agent’s workflow looks like this:
1. Generate search query.
2. Call search_arxiv. Receive list of papers.
3. Filter papers by date (current year).
4. For each paper, call get_paper_text.
5. Store text in vector memory.
6. Query memory for specific technical terms (e.g., “error correction,” “qubit stability”).
7. Synthesize summary based on retrieved snippets.
The difference is night and day. The structured agent is grounded in reality. It cannot drift because every step is anchored to an external data source. The RLM feedback loop ensures that if it generates a summary not supported by the retrieved text, it receives a penalty.
Implementing Structure: A Technical Perspective
For developers building these systems, the implementation of structure often falls into three layers: the Input Layer, the Processing Layer, and the Output Layer.
The Input Layer (Retrieval Augmented Generation – RAG): This is the first line of defense against drift. Before the agent reasons, it must retrieve. But simple vector similarity is not enough. We need structured retrieval. This means querying a knowledge graph or a relational database where the schema is known. This ensures that the context injected into the LLM is not just semantically similar, but factually accurate and structurally sound.
The Processing Layer (ReAct and Chain-of-Thought): We must enforce a reasoning trace. The agent should be prompted to output its reasoning in a structured format, such as XML tags:
The Output Layer (Schema Validation): Never trust the raw output of an LLM. It should always pass through a validator. If the agent is supposed to return a JSON object, use a JSON schema validator. If it fails, return the error to the agent as an observation. This creates a self-correcting loop. The agent learns that its output must conform to the expected structure to proceed.
This validation loop is a form of automated RLM. The “environment” (the code running the agent) provides immediate feedback on the validity of the output. This tight feedback loop is far more effective than periodic human feedback.
The Future of Structured Agents
As we push the boundaries of what agents can do, the reliance on explicit structure will only increase. We are moving away from monolithic models that try to do everything toward systems of specialized components. An agent of the future will likely look less like a single brain and more like a distributed system.
Imagine an agent where the “reasoning” model is a lightweight LLM, but the “memory” is a graph database, the “planning” is handled by a specialized algorithm (like a Monte Carlo Tree Search), and the “execution” is handled by verified code interpreters. In this architecture, the LLM is just one component—the creative engine—while the structure provides the stability.
This hybrid approach mitigates the weaknesses of pure LLMs. The probabilistic nature of the LLM is contained within the boundaries of deterministic systems. The agent can explore creative solutions within the safe confines of a verified plan.
We are essentially building the digital equivalent of the prefrontal cortex (structure, planning, inhibition) and the hippocampus (memory) to support the generative capabilities of the neocortex (the LLM). Without these supporting structures, the generative capability is ungrounded and prone to error.
Conclusion: Embracing the Constraints
The drift of unstructured agents is not a failure of intelligence, but a failure of containment. Intelligence requires boundaries to be effective. A river flows powerfully because it is contained by banks; without them, it becomes a stagnant swamp.
For engineers and developers, the lesson is clear: if you want reliable agents, you must build reliable structures. This means defining clear ontologies, implementing robust memory systems, and using RLM to shape behavior toward verifiable goals. It requires moving beyond the simplicity of “prompt and response” and embracing the complexity of system design.
The agents that will define the next decade will not be the ones that can talk the most, but the ones that can act the most reliably. And reliability is born not from scale, but from structure. By grounding our agents in the rigid realities of code, data, and logic, we allow their probabilistic capabilities to shine where they belong—in the gaps between the rules, not in the violation of them.
We must treat the agent not as a magic box, but as a component in a larger system. A component that is powerful, yes, but also fragile, and in need of careful scaffolding. The joy of building these systems lies in that scaffolding—in the precise engineering that turns a wandering mind into a focused tool.
