A long-running AI relationship faces two different demands. While a task is unfinished, it needs to resume accurately from what has happened. When a similar problem arises later, it should have an opportunity to change its approach. The first concerns finishing this piece of work; the second concerns doing later work better.
Both are often called “memory,” yet they require different records. A summary saying “we compared suppliers last time” cannot identify the run that is still waiting. A complete execution record does not automatically explain which method is worth carrying forward.
Teloa handles these two time scales separately. Work records preserve what happened; experience holds a revisable interpretation of those events. Sources connect them, and selected experience can then enter later work. Long-term collaboration needs both the connection and the distinction.
Resuming work starts with knowing where it stands
Consider a hypothetical supplier comparison. The first round lacks a quote and continues once it arrives. After delivery, the user asks for support conditions to be added. The goal remains related, but inputs and deliveries have changed several times.
The system needs to know which material is missing, which run has ended, and which version of the comparison was accepted. These belong to the original task and actual execution. Resuming from a historical summary alone may omit new material or repeat completed work.
Tasks, runs, and artifacts therefore retain their identities and relationships, while the native conversation and execution mechanisms of DeepSeek Harness (DSH) handle the process. Model completion and artifact saving are checked separately too. A result already obtained should be kept, and an unfinished save can be resumed; an interruption at the final step should not send the research back to the start.
Accuracy comes first when resuming work. What was approved and which version was delivered cannot change with a model’s new interpretation of history.
Experience needs room to reinterpret the past
Three revisions to a comparison may indicate an unsuitable role method, or simply a particular customer requirement. The original record alone rarely determines what should happen next time.
Interpretation requires judgment: what belongs only to this task, what may apply across a role, and what should become a shared project rule? An initial reading of feedback may itself be corrected by later evidence.
Experience and history therefore need different maintenance. History should remain as accurate as possible; experience needs to change. A requirement first understood as a lasting customer preference may turn out to belong only to a pilot stage. It can be withdrawn or reorganized while the original report and feedback remain.
Combining both in a continually rewritten summary lets the latest interpretation overwrite the past. Leaving everything unorganized forces the next task to search through every exchange again. Teloa preserves original evidence and maintains the experience drawn from it separately, accepting the additional work of managing sources and states.
Between organization and use, selection still matters
Auto Dream currently runs a separate daily role review, reads the corresponding evidence, and produces a digest with experience candidates. Eligible experience can be saved automatically under source and scope rules; suggested withdrawals remain decisions for the owner. Organization no longer has to happen within every delivery, and its interpretation remains open to inspection.
After automatic organization, valid content needs to participate in work. With the relevant project accumulation setting enabled and long-term memory permitted, the system can extract supported decisions after a conversation turn completes. Later related conversations automatically prepare project background and select applicable experience for the current input. Users need not search earlier discussion and explain confirmed requirements again each time.
Automatic inclusion requires selection. A project decision may not apply to another customer, and role experience has its own scope. Relevance, ownership, active state, and the input budget determine what reaches this turn. Automatic saving and actual use still have separate records.
Long-term work therefore involves three connected points:
| Point | Question it needs to answer |
|---|---|
| Original work record | What happened, and was the responsibility fulfilled? |
| Maintainable experience | What was understood from it, and where does it apply? |
| Current input | Which valid materials did this work actually receive? |
None can serve as evidence of effectiveness for the others. Saved experience may not have reached the current input. Included experience may have been misunderstood. Whether rework decreased still requires inspection of the delivery.
This also explains why a larger context window cannot solve memory by itself. Capacity allows more text to be sent; it does not decide which material remains valid, who it belongs to, or whether it fits this task.
Improvement needs a specific place to act
When a user says “don’t repeat this,” the product should help identify what can change. Outdated facts call for updated knowledge. A changed shared direction calls for a project decision. A repeatedly omitted check may call for an improved skill. They affect later work in different ways.
Appending every correction as another memory only accumulates more instructions and makes contradictions harder to locate. Separate maintenance lets a source change without changing a method, and a customer’s choice remain local rather than becoming every role’s default.
The aim is not to make users learn a classification system first. Maintenance should connect an actual problem to its content: correct the source when the evidence is wrong; inspect the method when a step is repeatedly missed. Categories should support correction rather than create another configuration task.
Evaluate growth in the next piece of work
Our expectation for long-term capability is that, when a similar problem recurs, relevant requirements can be obtained, the approach can change, and the delivery better meets the confirmed standard. Product records make that relationship easier to examine; they cannot guarantee it in advance.
An experience that repeatedly adds explanatory work deserves reevaluation. A reliably useful method deserves continued maintenance. A recorded past, revisable experience, and a new task that can test it offer a more useful form of progress than an ever-growing history.