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AI Readiness Scorecard

Answer and find out whether your company is ready to put an agent into production.

Step 1 of 4
1. Data foundation

Traceability that holds up beyond two intermediate systems, a single trusted source per entity, and refresh rates that match the time horizon of each decision.

Is there a single source of truth for the core entities (customer, account, product) that an agent will query?
Is data lineage documented, and does it survive more than two intermediate systems?
Is the freshness of the data the agent will use aligned with the decision's time horizon?
Is there a formal data quality process (validation, reconciliation) for the data feeding automated decisions?
Step 2 of 4
2. Integration

APIs and event streams that expose data through contracts under governance, with dedicated identity and limits for system-to-system calls.

Do the APIs the agent will call have a formal, versioned and documented contract (schema, rate limits, data classification)?
Does the agent act under its own traceable identity?
Is each credential the agent uses scoped to the narrowest set of operations the task requires?
Are rate limits and anomaly detection sized for non-human call patterns?
Step 3 of 4
3. Application architecture

Legacy systems built for batch processing and human review do not, by default, offer the interfaces agents need to write changes safely.

Do the legacy systems the agent needs to touch expose safe, governed write interfaces?
Is there a formal, documented criterion for deciding which legacy systems need modernization before an agent gets access?
Is the coupling map between the systems the agent will traverse documented and known?
Is there an operational risk mitigation plan for systems whose knowledge sits with one or two people?
Step 4 of 4
4. Observability

If the organization cannot follow, in order, what the agent read, decided and executed, with date, time and an owner for each action, automation creates a control gap.

Are accuracy, latency and escalation SLOs defined and agreed before the agent goes live?
Is there a named owner with authority over the agent's behavior in production?
Does observability cover the agent's actual behavior and decisions, including outputs and decision patterns?
Is there an incident response path designed specifically for silent agent failures, a subtly wrong decision repeated over time?
Data
0/8
Integration
0/8
Application
0/8
Observability
0/8
-
This scorecard reflects the four-dimension maturity framework TreeID uses in architecture reviews for Agentic Enterprise: data, integration, application and observability. As a Databricks partner, TreeID brings lakehouse governance and production-grade MLOps to the data dimension of this assessment.
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