Account team
What changes for the customer relationship?
Relevant context includes commitments, stakeholders, account strategy, and commercial risk. The system might prepare a briefing or propose a conversation; it cannot invent a new commitment.
Notebook · Philosophy
ThoughtSpace:
the context around
a role.
My way of thinking about what an intelligence needs to know, notice, and be responsible for.
The premise
A role is more
than a task list.
A role carries a point of view. An account director, an engineer, and a finance lead can look at the same event and ask different questions because they have different responsibilities.
I use ThoughtSpace to describe the context within which a role reasons: its objectives, relevant knowledge, current state, relationships, constraints, and available actions.
In this model, representing a role means making those things explicit. A prompt can describe an instruction; the wider ThoughtSpace describes what makes that instruction meaningful and what limits the response.
Mapping the role
What belongs in
a ThoughtSpace?
- Purpose
- The outcome the role is responsible for, and the tradeoffs it must respect.
- Knowledge & state
- Relevant records, policies, history, and current conditions, with their sources and uncertainty.
- Relationships
- The people, accounts, systems, and other roles whose actions or needs matter.
- Events
- The changes that should cause the role to revisit its understanding or consider an action.
- Authority
- What it may read, recommend, execute, or approve, and when it must escalate.
- Feedback
- What happened after a decision, what was corrected, and what should be tested before changing the system.
Figure 01
A role reasons inside a boundary
A role’s contextThe information and responsibilities relevant to its work
- Purpose and authorityWhat success means; what it may decide or do
- Knowledge and relationshipsRelevant records, sources, uncertainty, and commitments
- Events and feedbackWhat changed; what happened after the last action
One event, several perspectives
A customer launch
is delayed.
This example shows why a shared event does not imply a shared response. Each role needs a different slice of context and a different boundary of action.
Customer success
What changes in the adoption plan?
Relevant context includes milestones, dependencies, support history, and the customer’s goals. The system might flag a risk and draft a revised plan for review.
Engineering
What is blocking the launch?
Relevant context includes incidents, dependencies, release state, and technical ownership. The system might assemble evidence and route the issue, within access and change controls.
Finance
Which assumptions need revisiting?
Relevant context includes contract terms, billing milestones, and forecast assumptions. The system might flag a forecast for review rather than silently change financial records.
From context to motion
Events make
the model useful.
A ThoughtSpace that never changes is only a description. Events connect it to what is happening: a meeting ends, an incident opens, a payment arrives, or a dependency moves.
- An event identifies what changed and where the evidence came from.
- The relevant role’s context is refreshed.
- A capability proposes a response using that context.
- The surrounding system checks authority, constraints, and any required approval.
- The outcome becomes another event and a record for evaluation.
This is where my event-driven operating model begins. The model supplies reasoning; the system supplies boundaries, routing, and an inspectable history.
The event-driven operating model ↗Figure 02
A changing situation becomes a bounded action
- EventRecord what changed and its source.
- ContextRefresh the relevant role’s understanding.
- ProposalPrepare a response using that context.
- AuthorityCheck constraints, permissions, and required approval.
- OutcomeRecord the result as evidence and a new event.
The outcome refreshes context; proposed changes to standards still need review.
Where I think this goes
From isolated prompts
to situated systems.
I expect more of the value of AI to depend on the context around a model: the quality of its information, the responsibilities it is given, and how its outputs connect to real work.
In that future, a useful system might keep the context for a role up to date and bring a person the decisions that deserve attention. The interface could become a conversation about a situation rather than a tour through disconnected applications.
That is a direction I’m exploring, not an inevitable outcome. Context can be incomplete, models can be wrong, and an apparently coherent system can still optimize the wrong objective. I want those limits to shape the architecture from the start.
A distinction that matters
A design model,
an empirical question.
ThoughtSpace is my systems-design model for the context around a role. It is not a measured region of a neural network or a claim that human roles map directly onto model features.
My sparse-autoencoder paper examines a different, narrower question: how feature size, decoder geometry, and semantic specificity relate in learned representations. It informs my curiosity about geometry; it does not validate the role architecture described here.
Read about the research ↗Continue the notebook
From one role
to a whole organization.
Go deeper
See how I encode a role in RoleOS ↗