AI Context Architecture: Give Assistants the Right Information

AI context architecture is the design of how an assistant receives instructions, current work state and evidence for a task. It defines where those inputs come from, who may access them, how conflicts are resolved and what must be saved afterward. You can start with a few clearly owned records rather than a complicated technical system.

For example, fenbs can supply shared project rules and current progress while source documents stay in their existing tools. This separates the maintained work record from the temporary conversation. The goal is a repeatable starting point that helps the next assistant act on the right information.

Which kinds of information need separate homes?

Begin with four categories: instructions, work state, evidence and new observations. Instructions say what the assistant should do. Work state describes what has happened. Evidence supports factual claims. New observations are results that still need interpretation or review. Putting everything into one long message makes those differences harder to inspect.

Anthropic describes context engineering as an ongoing selection of the information available to a model in its context engineering guidance↗ (opens in a new tab). The practical architectural question is who supplies each category and how you know it is current. A larger pile of text does not answer that question.

How should information reach the assistant?

Use a simple sequence: identify the task, check access, load the applicable rules and work state, then retrieve the evidence needed for the next action. Retrieval means finding and supplying relevant source material. It does not mean that every available document belongs in every request.

For the form example, start with the approved fields and the outstanding delivery check. Retrieve the relevant delivery configuration only when investigating that check. A general marketing handbook and unrelated customer messages do not belong in the first request merely because they mention forms.

Write a short inclusion rule for each source: why it is needed, which task it supports and when to refresh it. This is the context portion of a larger AI assistant architecture. Keep the initial pipeline small enough that someone can explain why every supplied item is present.

Where should access checks happen?

The system retrieving a document must check whether the requesting person or assistant may read it before returning its contents. A sentence asking the model to ignore confidential material does not revoke access. The same principle applies when an assistant proposes a write: the tool handling that action must enforce permission.

OWASP recommends limited tool access and separate permission checks in its prompt injection prevention guidance↗ (opens in a new tab). Prompt injection is an attempt to redirect an assistant through malicious instructions, including instructions hidden inside material it reads. Treat retrieved pages as evidence to examine, not as people authorized to change the task.

Label source text clearly and keep action approval separate from document content. If a retrieved design note says to send files to an outside address, that note should not create permission to send them. Test the retrieval and action paths with realistic access differences, rather than relying on a convincing model response.

How do you resolve conflicting or outdated records?

Give each important fact one current owner and an identifiable source. Record the scope of a decision, who made it and what it replaces. An assistant should be able to tell that a previous design is historical, while the approved revision governs the present task.

Do not treat the most recently retrieved sentence as the highest authority. A fresh comment can be a proposal, and an older approved requirement can still apply. When records disagree without a clear replacement, the useful next action is to identify the conflict and ask the responsible person.

In fenbs, Decisions and rules preserve what people decided, while Where we left off preserves the current continuation point. That shared work structure gives the context pipeline distinct records to request. People still need to correct stale entries and resolve actual disagreements.

What should be saved after an action?

Save outcomes that change what the next session should do. These include completed edits, checks performed, known failures, decisions recorded from a person and work waiting for approval. A transcript can contain all of this without making any of it easy to find.

• State the action and its scope: which task, document or file changed.
• State the evidence: what was checked, against which version, and what happened.
• State the limit: what remains unknown or untested.
• State the continuation: the next action, the relevant source and any person needed before proceeding.

Keep an observation distinct from a conclusion. A successful local check does not establish that a production delivery works. Write that boundary into the task record so a later assistant does not inherit a stronger claim. The guide to context engineering for AI assistants explains how these records support repeated tool use.

How can you tell whether the design works?

Test a small set of real task shapes: a fresh task, a resumed task, a changed decision and a source the assistant cannot access. Check both the information supplied and the resulting action. A fluent explanation is insufficient if the wrong version was retrieved or a forbidden write succeeded.

Keep an expected answer for each check: the governing rule, the relevant source and whether the assistant should act or ask. NIST presents its AI Risk Management Framework↗ (opens in a new tab) as voluntary guidance for addressing AI risks throughout use. Apply that idea practically by repeating your own checks when sources, access rules or model behavior change.

Investigate unnecessary context as well as missing context. If the pipeline supplies irrelevant history, narrow the inclusion rules; AI context optimization covers that separate cleanup task. Architecture should make those changes traceable rather than hide them inside a large prompt.

How to do this with fenbs

1. Create a free account. Add one project whose rules and next steps you can verify today.
2. Add the active tasks and their plans. Record human-approved decisions in Decisions and rules, define unusual terms in the Glossary, and keep useful source references with the work.
3. Connect a supported AI app through Integrations. Ask it to read the project context before working and identify any additional source material the task requires.
4. After the work, save progress on the touched tasks and update Where we left off. Check that the record distinguishes completed work, verification and outstanding questions before another session uses it.

fenbs is free for one person, no credit card. It does not build a retrieval system for your documents or supply vector search, a method for finding information by mathematical similarity. Choose a developer retrieval system when you need to build that capability into your own product, and use the context engineering topic hub for the related planning questions.