Agentic AI: why your future agents first need a “data constitution”

Agentic AI projects are advancing quickly: autonomous agents, business copilots, no‑code automations. Yet most failures come less from the model than from the data and the pipelines feeding it.
This text explains why a “data constitution” is becoming essential, how to translate the Creed framework into concrete practices for SMEs/mid‑caps and scale‑ups, and how to integrate these principles into modern stacks combining RAG, vector databases, and no‑code/low‑code tools.

⚙️ Core idea: powerful agents, but very fragile as soon as the data drifts.

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1. Agentic AI: powerful agents… but dependent on their data

The new autonomous agents no longer just summarize text. They:

• trigger Zapier/Make/n8n flows,
• update databases (Airtable, Notion, managed PostgreSQL),
• drive IT tools, ERPs, or CRMs,
• enrich vector databases for semantic search and RAG.

🧠 Problem: an agent almost never questions the data it receives.

In a classic reporting context, a pipeline error produces a wrong KPI on a dashboard. A human eventually spots the absurdity.
With autonomous agents, the same error becomes an erroneous action:

• a support agent answers incorrectly because its vector store contains inconsistent embeddings;
• a purchasing agent confirms an order with an inactive supplier;
• an IT monitoring agent disables the wrong resource.

The model (GPT, Llama, others) is often solid. It’s the data quality and flow governance that determine operational reliability.

🛡️ Conclusion: before “giving more autonomy” to an agent, you must define the laws that govern its input and output data.

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2. From “data constitution” to the daily life of an SME/mid‑cap

graph TD
    A[User provides content] --> B[Assistant analyzes sections]
    B --> C{Is a visual helpful}
    C -->|No| D[Return text NOT_RELEVANT]
    C -->|Yes| E[Select best diagram type]
    E --> F[Design simple diagram 6-7 nodes]
    F --> G[Output Mermaid code only]

The VentureBeat article introduces the idea of a data constitution or Creed framework: a set of rules that legislate the data before it touches a model or a vector database.

For an SME/mid‑cap or a scale‑up, this translates into three very concrete pillars.

2.1. Minimal but explicit governance

📜 Goal: know which data is allowed to feed an agent, in what format, and with which guarantees.

“Lightweight” governance can be enough:

• Identified owners

  • one business owner per domain (support, finance, IT, purchasing);
  • one technical owner per critical pipeline (data engineer, developer, no‑code power user).

• Simple data contracts

  • authorized sources (CRM, ERP, helpdesk, monitoring, shared files);
  • required fields per type of event or record;
  • freshness thresholds (e.g. “stock data 🚦Principle: everything that goes in or out of an agent must comply with a testable contract.

Examples of input rules:

• Customer support

  • each ticket must have a valid client_id, a channel (email, chat, phone) and a language;
  • if a critical field is missing → ticket goes to quarantine, not visible to the agent.

• Purchasing agent

  • no purchase request without a referenced supplier_id and a valid cost center;
  • if unit price 🧩 Idea: place safeguards where teams enter and manipulate data.

Examples:

• Airtable

  • required fields, controlled value lists;
  • formulas to validate amounts, dates, statuses;
  • filtered “anomaly” views feeding quarantine.

• Notion

  • structured databases with required properties, relations between tables (clients, contracts, incidents);
  • simple rules via integrations (Make/n8n): if key field missing → send to a “To fix” database.

• Retool / internal low‑code tools

  • forms with strong constraints (regex, value ranges, references to a master table);
  • action buttons triggering an n8n/Make workflow with automatic validations.

The goal is to bring controls closer to where the data is created.
The earlier errors are blocked, the less they contaminate the vector store or the Data Warehouse.

3.2. Typical stack for a company without a large data team

A realistic, common setup:

• Data Warehouse: managed PostgreSQL, BigQuery, Snowflake, or equivalent;
• Sync/integration tool: n8n, Make, Zapier, or managed Airbyte;
• Application databases: Airtable, Notion, CRM/ERP, business tools;
• Vector database / RAG: Qdrant, Weaviate, Pinecone, PGVector, or RAG features from an LLM provider.

Each component can embed data constitution principles:

• checks in forms, front‑end validations;
• rules in workflows (conditions, splits, explicit errors);
• verifications in ETL/ELT jobs before writing.

🧷 Intended effect: automation remains no‑code in the interface, but “hard‑codes” quality rules into the pipelines.

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4. Use cases: how an agent can “go off the rails” without a data constitution

4.1. Customer support agent + RAG knowledge base

Context

• A company deploys a customer support agent:

  • answers FAQs;
  • drafts email replies;
  • suggests internal procedures for human agents.

• The knowledge base combines:

  • internal articles, FAQs, procedures;
  • anonymized historical conversations stored in a vector database.

Risks without a data constitution

• Outdated files not flagged as such → the agent recommends an old refund policy.
• Misaligned embeddings (nulls, API errors, encoding issues) → vector search returns passages unrelated to the question.
• Poorly tagged tickets (language, product, country) → the agent responds with the wrong contractual terms.

Concrete rules to implement

• Before writing to the vector store

  • verify that each document has a type, a validity_date, a version;
  • refuse indexing if critical fields are empty or if the document is marked “draft”.

• Embedding checks

  • store a fingerprint (hash) of the source text alongside the vector;
  • regularly ensure that each vector has an associated text and that the length/format is within the expected range;
  • on embedding API error → send document to quarantine, no partial indexing.

• RAG filtering

  • limit search to documents with status = validated and date_end_validity IS NULL or in the future;
  • forbid final answers if no source meets a minimum score → the agent answers “I don’t know” or escalates.

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4.2. IT monitoring agent and automatic remediation

Context

• An agent monitors metrics (CPU, latency, errors) and application logs.
• It can:
  • create incident tickets;
  • restart services;
  • adjust the capacity of certain cloud resources.

Risks without a data constitution

• Duplicated or badly timestamped events → the agent believes there is a critical incident, whereas it is a test or an old alert.
• Wrong service ↔ environment mapping → remediation action executed in production instead of pre‑production.
• Missing data (no severity provided) → decisions are made on the basis of incorrect thresholds.

Concrete rules

• Event schema

  • required: service_id, environment, severity, timestamp, metric_name, metric_value;
  • maximum accepted latency (e.g. 2 to 5 minutes) to consider an event as “real‑time”.

• Dead‑letter queue

  • any event without environment or a referenced service_id goes to quarantine;
  • no remediation based on an event from quarantine.

• Action safeguards

  • whitelists/blacklists of services allowed to be restarted automatically;
  • “heavy” actions (major scaling, cluster shutdown) requiring human approval or double confirmation.

Without these safeguards, a simple formatting or mapping bug can trigger a critical action on the wrong scope.

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4.3. Procurement/finance copilot for spend approvals

Context

• An AI copilot assists procurement/finance:
  • pre‑classification of expenses;
  • suggestions of alternative suppliers;
  • detection of duplicates or invoice anomalies.

Risks without a data constitution

• Wrong supplier_id ↔ legal entity mapping → recommendations based on an old supplier or the wrong entity.
• Misparsed amounts (currency conversion, decimal separators) → false fraud alert or missed budget overrun.
• Cost center inconsistencies → incorrect allocations in management accounting.

Concrete rules

• Input data contract

  • each invoice must have a standardized currency, a validated numeric amount, and a unique supplier identifier;
  • exchange rates from a single, dated source, otherwise send to quarantine.

• Output rules

  • approval suggestions with confidence indicators; if score