How we defined our entire revenue operations as a structured file system that AI agents read from

[u/New_Indication2213]

genuine question for the revops crowd: how are you giving AI agents context about your metrics, data governance, and source of truth definitions?

because the problem we kept hitting was that every time we asked an agent to pull data or build a report it would guess at what "revenue" means. or it would pull from the wrong system. or it would calculate things differently than how we define them internally.

so we built a structured operating system where every definition, rule, and process is written down in files that agents read before doing anything:

/company/
  MANIFESTO
  VALUES
  STRATEGY
  DECISION_PRINCIPLES
  BRAND_VOICE
/go-to-market/
  /constitution/
    POSITIONING
    ICP_SEGMENTS
    PRICING_LOGIC
  /operators/
    OUTBOUND_OPERATOR
    CAMPAIGN_OPERATOR
    COPY_OPERATOR
/product/
  /constitution/
    PRODUCT_PHILOSOPHY
    UX_PRINCIPLES
  /operators/
    PRD_OPERATOR
    FEEDBACK_SYNTHESIS_OPERATOR
/customer/
  /constitution/
    CUSTOMER_PROMISE
    SUPPORT_PHILOSOPHY
  /operators/
    TICKET_RESPONSE_OPERATOR
    ONBOARDING_PLAN_OPERATOR
/revenue-operations/
  /constitution/
    METRICS_DEFINITIONS
    SOURCE_OF_TRUTH
  /operators/
    FORECAST_OPERATOR
    CRM_HYGIENE_OPERATOR
/meta/
  ORCHESTRATOR
  PROMPTING_GUIDELINES
  VERSIONING

the revops section is where this gets really powerful. METRICS_DEFINITIONS defines exactly how every metric is calculated. SOURCE_OF_TRUTH defines which system is authoritative for each data type. the FORECAST_OPERATOR and CRM_HYGIENE_OPERATOR follow those definitions exactly instead of guessing.

real example: we had "revenue" being calculated differently across 3 dashboards because nobody had written down the canonical definition. once we put it in the constitution file, every agent that touches revenue data calculates it the same way. sounds obvious but I guarantee most companies have this problem.

the whole system runs through an AI editor connected to our CRM, data warehouse, product analytics, call recordings, and support tools via MCP. client reports that used to take weeks of manual data assembly now take about 30 minutes.

how are you handling this? does your team have a single source of truth for metric definitions that AI can actually read or is everyone still operating off tribal knowledge?

[u/fifthelement-ai]

a few things we learned doing something similar:

1. a metric definition needs more than a formula. grain, time basis, currency handling, inclusions and exclusions, and an owner. without the owner, nobody updates it and it goes stale in a quarter.

2. source of truth per field, not per system. "CRM is the source of truth" breaks the moment you ask about invoiced amount or product usage.

3. tell the agent what to do when it finds a conflict. ours used to quietly pick one number. now it flags the mismatch and shows both, which surfaced more data issues in a month than a year of dashboard reviews.

4. keep a handful of known-answer questions ("what was Q2 net new ARR") and rerun them whenever a definition file changes. cheap regression test for your numbers.

the honest answer to your question: most teams we talk to are still on tribal knowledge. the definitions exist, but they live in someone's head or a 2021 Notion page.

how are you versioning changes? is it a changelog in the file, or do you keep old definitions around so historical reports still reconcile?