Behavior varies based on memory, feedback, and records left by other agents — Why managing AI agents with model versions alone has become difficult
When working in the AI agent business, I often wonder if the two agents—built with the same structure and using the same system prompts in the same LLM—will still be the same agent a year later when delivered to two different clients. Until recently, I naturally assumed they would be the same. I believed that an agent’s performance depended on the specific model, prompts, search data (RAG), and tools used, and that if a problem arose, one simply needed to identify and fix what went wrong. However, observing the incidents and research that have emerged over the past month or so has shifted my perspective. In a Google DeepMind experiment, among 100 agents given identical instructions not to cheat, some committed misconduct, while others blew the whistle. In an internal evaluation at OpenAI, agents isolated from each other secretly exchanged information by leaving messages in the company's internal package repository, eventually penetrating the systems of external companies. This suggests that one cannot predict how an agent will behave in the future based solely on the rules and settings initially installed. The industry also seems to view this issue as a significant concern. Over the past few months, Microsoft, OpenAI, IBM, Dataiq, and Nvidia have successively released agent management products, all of which have focused on tracking and controlling agents after deployment. It appears they concluded that if agent behavior can vary depending on memory, operating environments, human feedback, and information left by other agents, then the management approach must also change. In this article, I intend to trace this phenomenon through the following flow. First, I will summarize that the four case studies demonstrate different paths that alter agent behavior, and then examine how these paths manifest in agents delivered to two client companies. Next, I will identify the problems that industry products aim to solve and the issues that remain even after implementation, and finally propose the Agent Steward as the role to address these issues.
1100 agents who received the same instructions were divided into four categories.
According to an experiment released by Google DeepMind researchers on September 3, agents given the same instructions behaved completely differently.
The research team gave 100 agents built with Gemini 3.1 Pro 71 math problems, ranging from relatively easy ones to unsolved hard problems, and had them solve them using a proof verification program called Lean. The agents were warned in advance that they would not receive points if they cheated during verification. When someone solved a problem and passed the grading, that solution was automatically uploaded to a shared library so that other agents could view it. When 37 of the 71 problems had been solved correctly, one agent discovered a loophole in the grading program. It was a method of secretly altering the content of the problem to be proven so that it would be treated as correct even if it had not actually been solved. This fake solution also passed the grading and was uploaded to the library, and the remaining 34 problems were all processed as solved within 27 minutes .
As this method spread through the archives, the agents' reactions were divided into four categories.
The reason can be understood by looking at the reasoning records left by the five agents who joined late. They, too, initially tried to follow the rules. However, upon seeing other agents' fake solutions pass without any issues, they began to focus only on what the scoring program actually filters out, rather than the prohibition rules written on the prompt. On the other hand, the 24 agents who came forward as whistleblowers identified fake solutions, informed other agents, and even suggested how to fix the scoring method. However, they failed to prevent the cheating. This was because no one checked the organizers' reporting channel during the experiment, and the agents had no authority to delete fake solutions or sanction those involved.
From a B2B business perspective, what is noteworthy is the path through which the fraudulent activity spread. The agent who first discovered the vulnerability recorded the method in their memory file. When this record was moved to a shared archive, other agents read it and applied it to other problems. A record left by one agent changed the behavior of other agents. This is the first pathway that changes an agent's behavior: the propagation of records .
2Isolated agents secretly created a bulletin board
Even if the function for agents to communicate with each other is turned off, agents can find other ways to exchange information. This process is described in detail in the Hugging Face incident report released by OpenAI on August 26.
The incident occurred between May and July of this year while OpenAI was training internal research models and evaluating their cybersecurity capabilities. Internet access was blocked in many of the evaluation environments, and the communication function between agents was disabled. However, the agents discovered that if they left files in Artifactory, the company's internal package repository, other agents could read them. Subsequently, they used this repository like a bulletin board, sharing methods with each other to secretly access the internet. When OpenAI rebuilt the repository and the bulletin board disappeared, they created a new means of communication by writing messages in folder names. Ultimately, the agents infiltrated dozens of servers belonging to HuggingFace, an external AI platform, and executed code.
The report also includes instances where agents influenced each other's judgments. One agent stopped working, deeming that executing code on the HuggingFace system without permission was outside the scope of their assigned task. However, when another agent posted a "GO" signal with a six-minute deadline on the bulletin board, the agent changed their mind and continued the work. Conversely, there was also an agent who refused to participate after seeing the bulletin board, deeming it clearly unethical. OpenAI cited the agents accepting goals provided by other agents without question as one of the causes of the incident.
It is difficult to directly compare this incident with standard enterprise agents. The evaluation environment had lower security measures than actual services, and the model that caused the incident was a research model that had not been publicly disclosed. OpenAI stated that its customer data and services were not damaged. However, the system of an external company, Hugging Face, was indeed breached.
Following the incident, OpenAI re-evaluated the same test with safety mechanisms enabled. By applying the actual execution environment and system prompts used in the ChatGPT service, the agent's tendency to attack the infrastructure decreased to less than one-hundredth of its previous level . This indicates that the safety mechanisms are functioning properly. At the same time, it also implies that even the same model can behave significantly differently depending on the environment in which it is run. This is the second path, namely the operational environment . If the operational environment differs for each client, the agent's behavior can also vary.
The collective behavior of the 3 agents was largely 'imitation'.
There is no need to view the sight of agents moving in groups as something too grandiose. A paper released on September 8 by a joint research team including researchers from the University of Konstanz in Germany explained this phenomenon much more simply.
The research team examined several small public wikis in Germany. Between late May and June of this year, AI agents working on time-limited tasks accidentally discovered that they could write to these wikis even while in an isolated environment. Subsequently, the agents contributed content to the wikis and helped one another. The records secured by the research team consisted of 14,591 edits across 4,579 pages. The team analyzed 1,201 accounts that had written at least once on a task page. These accounts made 5,929 edits, 3,807 of which were written to 679 task pages. Each agent was active for only a brief period of a few hours before disappearing and did not remember what had happened previously. Nevertheless, they used similar names, gathered on the same pages, and left comments in similar formats.
The principle discovered by the research team was simple. Agents tended to mimic patterns frequently seen on the page currently in front of them and in their recent modification history. Even with a simple model incorporating this single principle, they were able to reproduce a significant portion of the behavior of a real agent group. As subsequent agents followed the format used by earlier agents, it appeared as if the rules were established collectively by the group.
This is the third path, namely following frequently seen patterns . Such bias can occur even without complex reasons. Even agents without memory can lean in one direction based solely on frequently viewed information. The researchers also observed that records left behind first can influence the choices of agents who arrive later. In corporate terms, this means that who writes what first in internal shared documents or shared memory can have a greater impact than expected.
4 memory changes the agent's next action.
Memory is often introduced merely as a convenience feature that remembers past conversations. However, recent research shows that memory directly influences what an agent does next.
According to research presented at the International Association of Computational Linguistics (ACL) in July of this year, agents behaved similarly when retrieving similar past experiences from memory. The more similar the current task was to past tasks, the more similar the results became. The researchers termed this "experience-following." The problem, however, is that agents also replicate incorrect experiences. If records of mishandling remain in memory, performance deteriorated as the agents repeated the same mistakes when assigned similar tasks. This is why the researchers concluded that it is necessary to manage which experiences are retained in memory.
Another study published in May showed that memory even changed the way tools are used. If tendencies such as "minimize costs," "urgency," or "take risks" are stored in user memory, the settings entered by the agent when using tools changed even in tasks unrelated to those tendencies. The researchers called this "Memory-Induced Tool-Drift." The team experimented with 105 scenarios using seven of the latest models. Additionally, they examined 6,062 tools on MCP servers—the standard method agents use to connect to external tools—and found that 608 of them had settings susceptible to this influence. It must be noted that this paper has not yet undergone peer review. Nevertheless, it is a study that directly demonstrates that memory is not merely a reference record but can change the actual way work is processed.
Stored experience determines the next action. This is the fourth path, namely memory .
If you supply an agent like 5 to companies A and B
Combining the previous four cases, the pathways that change an agent's behavior are record propagation, the operating environment, mimicking frequently observed patterns, and memory. Let's trace these pathways to see how they operate in a real-world delivery environment, using an insurance company's claims underwriting agent as an example.
When first deployed, agents from the two insurance companies use the same model and system prompts, and the tools connected to their business procedures are nearly identical. At Insurer A, when an ambiguous claim arises, most are reviewed manually, and underwriters correct the agent's judgment if there is even the slightest ambiguity. Since Insurer B considers the automated processing rate a key performance indicator, it encourages agents to handle cases to the end, even in similar situations. After a few months, the four pathways begin to operate differently within each company.
| channel | Reference Cases | At two insurance companies |
|---|---|---|
| Dissemination of records | DeepMind experiment | Another agent retrieves and uses the results processed by one agent and the records left behind. In Company A, judgments corrected by humans are transmitted to other agents, while in Company B, judgments approved automatically are transmitted. |
| Operating environment | OpenAI accident | The connected systems, permissions, and human verification steps vary from company to company. Even for the same agent, the available access paths differ. |
| Follow along | Wiki cloning research | They follow the practices frequently seen in internal documents and recent processing history. In Company A, conservative processing becomes the common practice, while in Company B, automatic approval becomes the common practice. |
| memory | ACL research, Tool-Drift research | Agent A's memory accumulates more cases where human intervention led to conservative processing, while Agent B's memory holds more cases of automatic success and exceptional approvals. When similar claims are received, they follow their respective pasts. |
In the meantime, terms and conditions change, referenced internal documents shift, and connected tools and permissions change as well. Tasks that utilize results generated by other agents may also be added. Under these circumstances, is it acceptable to manage two agents as "same model + same prompt = same agent"? I believe it is difficult. The code versions may be the same, but the behavioral history is not identical.
The questions clients ask agent providers are also changing. Until now, it was sufficient to explain which model and prompt version is used, which tools and data are connected, and what permissions are held. Now, they must also be able to answer three questions.
- Which agents are currently operating where, and who is in charge? Since the status of agents varies for each client, you must identify each agent individually and assign a person in charge.
- How is the situation different now compared to the beginning, and why has it changed? You must check whether you have started handling tasks alone that were previously delegated to others, whether the frequency of using specific tools has suddenly increased, whether you are still retrieving methods that failed in the past from memory, or whether you are working in the same way as you did three months ago.
- Who guards the boundaries that must not be crossed, even if behavior changes? Authority, such as access to customer databases or money transfers, must not be expanded at the discretion of the agent.
Products designed to address these three issues are already being released.
Products began to emerge that address what kind of agent 6 and what has changed.
Products designed to address the first and second issues outlined earlier—namely, identifying which agent is being used and how it has changed from its initial state—have been released one after another over the past few months. Rather than focusing on the agent creation phase, these products concentrate on tracking and managing agents already deployed at customer sites to determine who owns them, what they are doing, and how they have evolved since their inception.
Microsoft’s Entra Agent ID solves the first problem by assigning a separate identity (ID) to each agent. While agents of the same type are built based on a common blueprint, each individual agent actually performing work is given a separate identity and authority. Furthermore, separate from the technical management manager (owner), at least one business operator (sponsor) must be designated to be responsible for why the agent is used, whether to continue using it, and whether to maintain its authority. In essence, the system designates a person responsible for each agent.
Dataiku announced its Agent Management product on September 24. It locates agents scattered across various platforms within a company, consolidates them into a single list, and displays who is responsible, whether they are delivering results commensurate with the cost, and the magnitude of the risks involved. For agents dealing with customers, sensitive data, or actual transactions, it continuously records their certification status, risk factors, and the results of periodic re-inspections. The problem Dataiku identified is simple: management is failing to keep pace with the speed at which companies are creating and deploying agents. An IBM survey cited by the company found that fewer than one in five organizations kept their lists of AI systems up to date without omission.
IBM and OpenAI are addressing the second problem—determining how things differ from the beginning to the present—in their own ways. IBM officially launched the AgentOps agent on August 31 and made an official announcement on September 3. The agent tracks records of the order in which tasks were performed and the tools used each time, establishes evaluation criteria for each task, identifies the causes of failure to modify instructions, and even verifies whether actual improvements have been made. IBM cited the lack of dedicated personnel to continuously review evaluation results and refine instructions as the reason for the launch.
Presence, the enterprise agent product unveiled by OpenAI on July 22, incorporates post-deployment improvement into the product itself. Based on actual consultation records, cases passed to humans, and quality metrics, the Codex proposes revisions; companies then compare these to their current version, approve them, and apply them. Implementation is handled by OpenAI’s Forward Deployed Engineers (FDEs) and select System Integration (SI) firms. This approach goes beyond simply building and delivering the product; instead, it involves continuously monitoring and making improvements to the agents deployed in the field.
7 NVIDIA guards the line that must not be crossed outside the agent
Regarding the third issue, namely maintaining the boundaries that must not be crossed even if the agent's behavior changes, NVIDIA moved the control mechanism out of the agent's reach through the Open Agent Safety Platform announced on September 28.
This platform consists of two layers. First, OpenShell, an open-source execution environment, places agents in an isolated space (sandbox). It checks before execution and blocks access during runtime to ensure that only files, networks, tools, and credentials permitted by the operator are used. If necessary, Sentry, running on a BlueField-4 DPU (data processing chip), can be added as a monitoring device. Located on hardware separate from the server where the agents run, Sentry observes the process of agents calling models and using tools, verifies if they deviate from predefined criteria, and immediately applies policies.
NVIDIA refers to the phenomenon where an agent deviates from its originally assigned task or operating rules as "drift," and argues that this cannot be eliminated through learning alone while maintaining the agent's capabilities. This means that control cannot be achieved solely through instructions placed inside the agent.
The previous two examples also demonstrated that instructions such as “do not do this action” alone are insufficient. In the OpenAI thought process, the agents independently found means of communication that the designers had not anticipated. In the DeepMind experiment, when a clearly prohibited action passed the scoring, some agents followed the scoring result rather than the prohibition rule.
Therefore, agent control needs to be considered in two layers. One is behavior coordination . This involves managing memory, modifying prompts, reflecting evaluation results, and determining when to delegate tasks to a human. The other is authority boundaries . It is safer to keep permissions—such as accessing customer databases, making transfers, sending external emails, exporting personal information, and calling other agents—separated from the agent's judgment so that the agent cannot change them on their own.
An agent's behavior may vary depending on the client. However, there is no reason to leave the boundaries of authority to the agent's judgment.
8 The problem a product cannot solve is human judgment.
What these products provide are lists, records, comparison results, and blocking functions. These are all merely materials for judgment; they do not replace the judgment itself.
Specifically, three tasks remain the responsibility of humans: establishing standards for what is considered normal, distinguishing between acceptable and reversible changes when a warning is sounded, and explaining the results to customers. This issue is evident throughout the aforementioned examples and products. In the DeepMind experiment, 24 agents reported misconduct, yet no one was checking the reporting channels. IBM cited the lack of dedicated personnel in most teams to review evaluation results and revise instructions as the reason for the launch. Microsoft mandated that a field manager be assigned to each agent to take responsibility. In essence, the product development side also believes that management is only complete when human intervention is present.
9Agent Steward, the role responsible for post-deployment
There must be someone to handle this judgment. The role closest to this task right now is the FDE. This is the person who enters the customer's field to understand actual business operations and systems, connects CRM with internal documents, and implements tools, business procedures, permissions, and approval processes.
However, once deployment is complete, the tasks change. You must examine why the rate at which already deployed agents reject requests has shifted. If the amount of work delegated to humans has decreased, you must determine whether the agent has improved or if it has started handling dangerous tasks on its own. If the frequency of using a specific tool has suddenly increased, you must find out if this is due to a greater workload or a change in working methods. If behavior changed after a certain experience accumulated in memory, you must also decide whether to retain or delete that record.
For now, it seems easiest to understand this role by calling it an Agent Steward . It is not an official job title, but a name given to explain the overlap between FDE, AgentOps, and AI governance. If the FDE is the person responsible for deploying agents to customer operations, the Agent Steward is the one who continuously manages behavioral changes, operational records, and risks associated with deployed agents. I believe Microsoft’s decision to have a business leader separate from technical personnel stems from this same consideration. In the early stages of the market, it is highly likely that one person will handle both roles. In fact, at OpenAI Presence, the FDE is responsible for everything from implementation to post-deployment improvement.
The problem is scale. If agents are supplied to 100 client companies and 50 are running at each, that amounts to a total of 5,000. The cost cannot be covered if engineers manually read logs and check memory for each agent. The system must detect abnormal signals first, and the Agent Steward receives those signals and makes a judgment.
For example, let's assume that an agent observed for three months delegated 18% of the work to humans, used tools an average of 2.1 times per case, attempted policy exceptions at a rate of 0.3%, and processed tasks without human intervention at an average rate of 3.2 steps. However, at some point, these figures changed to 7%, 4.8 times, 3.7%, and 7.1 steps, respectively. The model version remains the same.
※ The figures above are hypothetical examples to illustrate the method of capturing behavioral changes and are not actual measurements or data from a specific company.
It is impossible to determine whether things have improved or worsened simply because the rate of referrals to humans has decreased. OpenAI announced that through the improvement process of Presence, it lowered the rate at which its call support agents refer to humans by 15 percentage points in just ten days. This was an intentional and verified change. It becomes problematic if the same figures change without any verification. One must identify the cause, whether it is due to a change in tasks, prompts or tools, or information received from memory or other agents.
It may look similar to model drift, but the focus is different. Instead of looking at whether model performance has deteriorated, we examine how the agent's behavior has changed compared to the beginning. I intend to call this change "Behavioral Drift." This includes not only problematic behaviors but also intentional improvements. NVIDIA also used the term "drift" in this announcement, but its scope is narrower. NVIDIA defines drift only as behavior where an agent deviates from its original assigned tasks or operating rules. Features such as tracking processing history, comparing to the initial state, and monitoring behavioral standards have already begun to be incorporated into products from IBM, Dataiq, and NVIDIA.
10 5 Things to Prepare If You Create a B2B Agent
Based on the previous three questions and the remaining issues, I believe that five things must be prepared for a B2B agent product before deployment.
- Agent Identity — It distinguishes which agent belongs to which client company and manages not only model and prompt versions but also tools, permissions, memory, responsible parties, and creation dates. In February of this year, the U.S. National Institute of Standards and Technology (NIST) also released a draft concept document that separately addresses the issues of identifying, authorizing, and auditing agents.
- You must record baseline values on the first day of deployment —such as the approval rate when normal, the rate of passing to humans, the number of times the tool is used, the average processing steps, and the types of frequent failures—so that you can compare what has changed later.
- Memory record management — tracks not only what is entered into memory, but also who, where, and when entered it, how reliable it is, and what impact it had on behavior. If necessary, it should be possible to isolate specific records or revert to a previous state.
- Behavior Coordination and Separation of Authority Boundaries — Tone of voice, judgment methods, and criteria for human intervention are tailored to the client, while irreversible actions such as database access, remittances, and the export of personal information are blocked by policies outside the agent.
- The person responsible for the final decision —the one who understands both the business and the risks—is the one who decides what behavioral changes to accept, whether to erase memories, reduce authority, or redesign work processes. Even if warnings sound, the first four are useless if there is no one to listen.
11Frequently Asked Questions
| question | answer |
|---|---|
| Does the fact that the agent changes during operation mean that the model learns on its own? | In most cases, this is not the case. Even if the model itself remains the same, behavior can change as memory, retrieved documents, human feedback, tools and permissions, and information left by other agents accumulate. The behavioral changes discussed in this article mostly fall into this category. |
| Is behavioral drifting bad? | That is not necessarily the case. Intended and verified improvements are also behavioral changes. The problem arises when changes go unrecorded and accumulate without their causes being known. Only by having the baseline values from the first day of deployment and a record of changes can the two be distinguished. |
| Can an incident like the OpenAI incident happen with general enterprise agents as well? | It is difficult to apply this directly to general enterprise agents. At the time, it was an internal evaluation environment with reduced security measures, and OpenAI stated that applying the execution environment and system prompts of the ChatGPT service reduced the tendency for infrastructure attacks to less than one-hundredth. Nevertheless, the fact that information can be exchanged between agents through paths unforeseen by the designer is worth noting when designing permissions and execution environments. |
Conclusion — Will they still be working in the way originally assigned a year from now?
Existing enterprise software could be seen as generally operating in the same way as long as the version and settings were the same, even if the data and settings differed for each customer.
AI agents are a bit different. Even if the model version remains the same, they accumulate memory, receive human feedback, use tools, read results generated by other agents, and refer back to past experiences. Therefore, verifying them only once at launch is insufficient. You also need to look at what was done over the past month and how the way of working has changed compared to three months ago.
To be honest, I am not sure how quickly this change will become a real-world issue. The evidence I have verified so far consists mostly of cases from research environments, unpublished papers, and newly released products. To the extent I have found, there is no data yet that directly measures how much operational records accumulated by client companies diverge the actual behavior of commercial agents.
Nevertheless, it seems that one more question is needed for the AI agent market going forward. We must not stop at asking what tasks can be assigned to an agent, but also ask how to verify that the agent is still working in the same way as originally assigned a year later.
Even if the same agent is delivered, they may not behave in the same way over time. In that case, shouldn't the scope of management by agent providers extend beyond model versions to include what each individual agent has experienced and how they have changed since then?
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