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Module 06 · Lesson 21

Build a source-of-truth map

Document where each fact starts, which system owns it, and how a correction flows back.

Last updated August 5, 2026•15–25 minutes•Free AI agent course
What you will learn

Make a clear, safer operating decision.

You will be able to prevent an agent from mixing stale, duplicated, or unauthorized information by mapping every fact it relies on.

Why this matters

Good agent work is useful before it is impressive.

Most agent errors that look like ‘hallucinations’ begin with ordinary operations problems: a cancelled appointment remains in one tool, a policy lives in an outdated PDF, or a salesperson edits a field that another system overwrites.

Field note 21

Make the relationship visible.

AI AGENTS · FIELD NOTE 21Origin → authority → consumer → correctionTHE FACT MAP01Source02Authoritative record03Agent context04Correction ownerOriginal visual framework for Build a source-of-truth map.AI AGENTS · FIELD NOTE 21Origin → authority → consumer → correction01Source02Authoritativerecord03Agent context04Correction owner
Use this framework to make build a source-of-truth map visible before you build.
Core concepts

The language that keeps the work clear.

Data originWhere a fact is first captured or created.
AuthorityThe system and owner that decide whether a fact is current.
ConsumerA person, agent, report, or integration that uses the fact.
Correction loopHow a known mistake is fixed at the authoritative source and reflected elsewhere.
The practical method

Work through the decision in order.

List required facts

Start from the agent contract: customer identity, preference, status, policy, availability, owner, or history.

Assign an authority

For each fact, name the system and role that can validate or correct it.

Mark access direction

State whether the agent reads, proposes an update, or may write an approved field.

Test a correction

Change a sample fact at its source and verify what the agent and a human would see afterward.

Worked example

A realistic, bounded implementation.

A travel business wants an agent to help with booking changes. The itinerary platform owns booking state, the CRM owns relationship history, and the policy centre owns refund rules.

The agent may read an itinerary and CRM notes after verification. It cannot rely on a copied status field if the booking platform says the change is pending. A booking correction is made in the itinerary platform, then surfaced to the CRM as a recorded update.

The map gives operators a way to diagnose disagreements instead of blaming the model when the inputs conflict.

Build it in practice

Use this copyable working template.

Adapt it to the client’s evidence, policy, people, and tools. Do not treat placeholders as approved instructions.

Fact: [name]. Origin: [system]. Authoritative source: [system + owner]. Agent access: [read / propose / write]. Consumers: [list]. Correction procedure: [steps].
Spacebrain implementation

Put the operating system around the agent.

Use CRM fields, contact timelines, integration ownership, task notes, and named workspace roles to document which record is trusted and who fixes it.

Practice

Before you move on

  • Map five facts used by the pilot.
  • Find one duplicate field with no clear authority.
  • Run one correction from origin to agent output.
  • Every important fact has an authority.
  • Agent access follows the job, not convenience.
  • Conflicting sources have a defined resolution.
  • Corrections are tested, not assumed.

Build the operating layer around your agent.

Use the free Spacebrain workspace to keep contact context, handoffs, tasks, automation, and reporting together.

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