When sales representatives work with AI to revise a quotation, they might first agree on a maximum discount, then add customer-specific conditions, and finally decide to prioritize the delivery schedule. As more information accumulates in the conversation, AI may begin to overlook the originally requested format, reuse prices from before a correction, or even mix in conditions from another version. At that point, simply adding “Please remember the previous rules” may not get the task back on track.
Enterprises should manage three things separately: teams need to maintain reviewable versions of established rules, ensure that responses are grounded in authoritative sources, and properly hand off work in progress. Long conversations can still be used for discussion, but they are not suitable for storing enterprise knowledge or operating procedures.
Why Does AI Start Missing Earlier Information as Conversations Get Longer?
Business Next’s summary of observations by Mike Taylor, Head of AI Evaluations at Every, notes that the continued accumulation of old instructions, previous mistakes, and irrelevant details can gradually degrade the quality of AI responses. This is known as context rot. Examples provided earlier in a conversation may also constrain subsequent answers. Taylor once asked AI to imitate Steve Jobs’ style when naming products. With text instructions alone, 21% of the names began with “i.” After he added several examples of that kind, the figure rose to 100%. The examples were intended to help AI understand the desired direction, but when they remain in subsequent tasks, they can instead limit the answers.
Adding requirements incrementally during a conversation can also create conflicts between earlier and later instructions. In 2025, researchers from Microsoft and Salesforce split complete instructions across multiple messages to simulate users providing information in stages. Across six types of tasks, including coding and summarization, the tested models’ performance declined by an average of 39%. AI may also overlook important requirements buried in the middle of a conversation or document set. In one test, Taylor asked AI to read nine project documents and placed a critical bug report in the sixth. When he asked about project risks, the AI did not mention the bug at all.
Therefore, when AI repeatedly makes the same mistakes, overlooks information in the middle of the conversation, or begins violating the original rules, the implementation team should consider starting a new conversation. When any of these signs appear, avoid adding more messages immediately. Instead, check whether the current task has become mixed with previous objectives, irrelevant documents, or contradictory requirements. Then decide whether to revise the existing messages or summarize the current progress and continue in a new conversation.
Established Rules Cannot Be Given Only Once—How Should They Be Stored?
Word limits for customer service responses, the sequence for quotation reviews, fields used in procurement comparisons, and quality-control criteria may all require an AI Agent to follow the same procedures every time. If a team states these requirements only at the beginning of a conversation, the AI may gradually stop following them as the conversation grows longer. The original article also describes a user asking AI to follow the PEP 8 Python coding style guide. After 50 messages, however, the AI was no longer adhering to those standards.
Teams can begin by identifying the instructions that employees repeatedly give AI, then separating them into “established rules” and “task-specific requirements.” Fixed formats, mandatory checks, decision sequences, and required fields are all established rules whose versions should be centrally maintained by designated owners. Customer names, the delivery schedule for a specific assignment, and temporary restrictions should remain within the individual task. This approach allows teams to clearly track which version has changed whenever rules are updated, without expecting AI to retrieve the correct procedure from a conversation that took place long ago.
EgentWrX’s Skills (AI Skill) mechanism can store practices and rules that must be applied consistently and invoke them in relevant usage scenarios. Employees can also specify a Skill within a conversation. If an employee-created Skill is to be rolled out across the company, it must first pass a back-end review. Enterprises can begin with one frequently repeated task that has clearly defined criteria. List the rules employees remind AI to follow every time, assign a maintainer and reviewer, and then turn those rules into a Skill. Conditions that apply only to the current task should not be included in the Skill.
Where Should Authoritative Sources and Work Progress Be Stored?
Preventing context rot does not mean that less information should always be included in a conversation. If too little information is provided, AI may make assumptions about what is missing and ultimately offer only generic recommendations that do not reflect the company’s actual circumstances. Enterprises can designate policies, product specifications, contract templates, and historical spreadsheets as authoritative sources and manage them separately from individual conversations. Whenever assigning a task, specify only the scope of information that needs to be searched, and clearly state the task objective, constraints, output format, and completion criteria in the first message.
EgentWrX’s knowledge base can manage enterprise data in three formats: Wiki, vector, and table. It also allows teams to set similarity thresholds and minimum citation requirements. If an AI Agent cannot find sufficient supporting evidence, it will decline to answer rather than fabricate a response. Implementation teams should test two scenarios in practice: when authoritative documents are available, can the response cite the correct sources? When the required information cannot be found, can the AI Agent decline to answer and clearly identify what information is still missing? Compared with placing every document into every conversation, this approach makes it easier to verify where the supporting evidence for an answer comes from.
For work that takes an extended period to complete, implementation teams must also consider how it will be handed off. When AI begins repeating mistakes, omitting information, or ignoring established rules, the team can first prepare a handoff document and then start a new conversation. At a minimum, the handoff document should clearly state confirmed facts and their cited sources, decisions already made, unresolved issues, and next steps. If sales quotations, procurement comparisons, or cross-departmental approvals recur regularly, they can also be turned into task cards or workflows. Human approval can be added at stages where an amount exceeds the department’s limit or conditions are unclear, pausing the process until the person responsible confirms that it may continue. Enterprises can begin with one task that spans multiple days, establish a standard set of handoff fields, and then test whether a new conversation can continue and complete the remaining work using only the handoff document and authoritative sources.
FAQ
Is there a specific number of messages after which a conversation must always be restarted?
Do not rely on the number of messages alone. If AI begins repeating errors that have already been corrected, overlooking important information in the middle of the conversation, or failing to follow the original rules, stop adding more content. First summarize the confirmed facts, cited sources, decisions already made, and next steps, then continue the work in a new conversation.
What should be included in the first message?
The first message should clearly and concisely state the task objective, essential background, constraints, output format, and completion criteria. It should also specify which authoritative sources the AI should search. Do not include old examples, historical discussions, or backup documents that are unrelated to the current task, as they may interfere with the AI’s judgment.
What is the difference between established rules and a handoff document?
Established rules cover formats, validation criteria, and decision sequences that must be followed every time, and their versions should be centrally maintained by designated owners. A handoff document records the known facts, decisions already made, unresolved issues, and next steps for a specific task. Once the task reaches a stopping point, the team can decide whether to retain the document based on actual needs.
Will starting a new conversation cause important information to be lost?
As long as the handoff is completed before a new conversation begins, the necessary work progress can be preserved. The handoff should include authoritative sources and clearly distinguish confirmed information from details that still require verification. Bring only the handoff, currently applicable rules, and necessary documents into the new conversation to avoid carrying over previous mistakes and irrelevant details.
References
- Is Your AI Getting Worse the More You Talk to It? An Expert Reveals Seven Signs of “Context Rot”: When Should You Start a New Conversation, and How Can You Get Better AI-Generated Content? — Business Next summarizes the warning signs and research behind context rot, along with practices for starting a new conversation.
