Minimum Viable Context: the real Unit of Data Readiness for Agentic AI

🔗 **AI Plugged — Episode #5 **— by Ana Maria Bezerra Maimoni

Visit the AI Plugged series homepage to explore this fascinating series on practical AI!

The hard part isn’t the agent. It’s the decision-ready slice of data and policy you’re willing to automate — and defend.

Transforming a failing “smart assistant” experience into a governed, high-impact agent

Most enterprises look “AI-ready” on slides. Very few can write one honest page that explains: For this agent, in this workflow, which data it uses, under which rules, and who will defend it to a regulator.

A recent BCG study found that only about 5% of more than 1,200 companies are achieving measurable business value from their AI investments. In comparison, around 60% see little to no benefit — often because AI isn’t wired into workflows, governance, and data foundations. Business Insider

This episode argues a simple, slightly uncomfortable point:

If you can’t describe an agent’s Minimum Viable Context on one page, you’re not ready for production — you’re doing AI theatre.

I’ll build on the governance lens from Episode #3 and the change lens from Episode #4, and focus on the missing middle: data readiness for agents that actually act.

From Smart Demo to Disappointment

You’ve probably seen this story.

The first “agent” pilot impressed everyone — answering questions, surfacing data, suggesting next steps. Then came production reality.

Once deployed, the agent stumbled: wrong records, stale fees, ignored policies. Not less intelligent — just colliding with real-world data chaos.

That’s the uncomfortable truth about agentic AI in large organizations.

Enterprises have modern stacks, yet stumble on a simple question: “For this agent, which data matters, and why trust it?”

When software acts for you, ‘roughly knowing’ isn’t enough. Know it, or don’t automate.

Here is where most data readiness conversations break down. We talk about single sources of truth and golden records, or enterprise data strategies, while teams building agents are trying to answer something much more concrete. For this one decision, in this one workflow, what is the minimum data and context an agent must have to be safe and valuable?

That’s where Minimum Viable Context (MVC) comes in!

🧭 Quick Summary for busy readers

Most enterprises chase “AI readiness” but fail when agents meet messy, ungoverned data. The article introduces Minimum Viable Context (MVC) — the smallest, well-understood slice of data and policy needed for an agent to make safe, defensible decisions. Key ideas:

• AI readiness isn’t about perfect data lakes but decision-ready context fabrics tied to real workflows.
• MVCs are micro data products that combine entities, rules, and guardrails for each agentic decision.
• The five myths (e.g., “single source of truth,” “fix all data first”) stall progress; MVC reframes readiness in terms of action rather than architecture.
• The five-step MVC playbook: start from the decision → define AI features → build the context → codify governance → monitor and scale.
• A field case from a retail bank shows how moving from AI theatre to MVC-based governance turned a stalled assistant into a compliant, measurable agent.

Bottom line: If you can’t describe your agent’s data, policy, and guardrails on one page, you’re not ready for production.

Stick around for the full story—you’ll take a ladder, a blueprint, and a real story to leverage. It’s definitely worth the read!

The Ladder of Data Readiness — and Why it isn’t a Straight Climb

Before diving into MVC, it’s essential to identify where most enterprises currently stand. The majority aren’t starting from a greenfield. They’re starting from a patchwork: warehouses and lakes, SaaS tools, core systems, spreadsheets, and half-finished transformations.

Think of a ladder of data readiness.

Most organisations live somewhere between Levels 1–3. A few workflows, in a few domains, quietly reach Level 4

Level 1 — Data Swamp with Scripts

• Data lives in scattered systems, shadow databases, and spreadsheets.
• Integration relies on CSV exports, ad-hoc joins, and brittle scripts.
• Governance lives in email threads and tribal knowledge.
• AI can do demos and isolated pilots.

As soon as an agent touches a real workflow, it hits missing fields, inconsistent IDs, and stale records.

Level 2 — Dashboard Era

• Key Data lives in a warehouse/lake, widespread dashboards, and BI.
• Some ownership, quality checks, and governance.
• Great for “what happened?” and “why did it spike?”

It’s real progress, but operational systems aren’t easily accessible to agents, and policies are either in PDFs or in people’s minds.

Level 3 — Logical Layer

• A logical or semantic layer over multiple sources.
• Consistent business entities modeling (customer, product, policy, account, asset).
• Data virtualisation/fabric provides governed, near-real-time access without copying everything.

AI can finally do meaningful retrieval and reasoning over journeys, not just tables.

Level 4 — Agent-ready Context Fabric

You construct purpose-built context bundles tailored for specific agentic workflows atop warehouses, lakes, and logical layers.

• core entities,
• relevant history and signals,
• applicable policies and constraints,
• quality and lineage indicators,
• clean integration surfaces to act (APIs, tools, workflows).

These are not generic “enterprise models” but actionable, decision-ready slices tailored to industry-specific needs:

• Banking: accelerate credit decisions, enhance fraud detection, optimize collections.
• Insurance: streamline triage process and improve case routing efficiency.
• IT services: enable automatic triage and resolution for recurring patterns.

Two important clarifications:

1. It’s a portfolio, not a journey of devotion. You don’t “finish” Level 1, then Level 2, then Level 3 across the whole enterprise. In fact, you’ll always have messy areas and more mature ones.
2. You don’t require Level 4 in all situations. Level 4 only where agents will actually take decisions and actions that matter — touching customers, money, or safety.

So, what’s the real unit of data readiness?

Minimum Viable Context — Beyond Data Products

Data leaders ask: ‘We already have governance and feature stores — what’s new?’ MVC reframes them around decisions.

What is Minimum Viable Context?

MVC defines the smallest, trusted slice of data + policy needed for an agent to act safely and effectively.

Not ‘all data.’ For each decision: which entities, sources, freshness, and guardrails — and who owns them.

How MVC relates to other ideas

1. Versus Data Products / Data Mesh

• Data products often serve broad analytical or domain needs.
• MVCs are decision-centric micro data products: they exist to support a specific automated decision, with policy and monitoring baked in.
• In a Data Mesh world, MVCs sit inside domains as a consistent pattern for AI decisions.

2. Versus Feature Stores

• Feature stores focus on model inputs at training and serving time.
• MVC wraps around that: it defines which features are allowed, from where, under which rules, and how their use will be governed and audited.

3. Versus Model Governance

• Model governance covers the lifecycle of a model: validation, bias checks, documentation, and approvals.
• MVC sits one step earlier: it defines the allowed decision + context slice that any agent or model can operate on in the first place.

In practical terms, MVC is a one-page blueprint for each agent:

• decision + scope,
• AI features used,
• context slice,
• governance and risk,
• monitoring and reuse.

And once you have a few MVCs, you don’t just have more templates. You have a pattern library for your most critical agentic decisions.

Five Myths that keep you Stuck

These myths show up in almost every steering committee once the first agent pilots land.

Myth 1: “Once we have a single source of truth, we’ll be agent-ready.”

Good instinct, bad framing.

Reality:

• Multiple systems will always matter for a given decision.
• Different agents need different views of the same entities.
• “Truth” depends on the decision — fraud, marketing, risk, and service may all need different slices.

Agents need clear, decision-specific slices — not one universal truth. That’s what MVC formalises.

Myth 2: “Buying an AI-ready data platform makes us AI-ready.”

You can have: a lakehouse, a semantic layer, a vector database, and a shiny AI platform…

… and still not answer: “How will our collections agent decide whether to propose a payment plan, escalate, or pause — step by step, in data and policy terms?”

When agents drive actions, readiness is measured in narratability.

Can you explain, in plain language, how an agent will discover, trust, and utilize the data it acts on? If not, the stack is just latent potential.

Myth 3: “We must fix all our data problems before scaling agents.”

Your data will always have issues.

For agentic AI, you don’t need everything perfect. You need:

• the data that matters for each agent’s decisions to be good enough,
• and well understood.

Start from workflows, not inventories; improve what matters, ignore the rest.

If you wait for perfection, competitors will learn by doing while you cleanse.

Myth 4: “Data readiness is a data-team problem.”

• Business says, “Tell us when the data is ready.”
• Data says, “We’re working on quality and governance.”
• Nobody co-owns the decision flows where agents operate.

It may be the most dangerous, as agents intersect business outcomes, data and integration, policy and risk, and working methods and incentives.

If any of these runs solo, you get either promising pilots that never reach production or production agents that quietly accumulate risk.

Myth 5: “More canvases mean more progress.”

A real concern: “If we create an MVC Canvas for every workflow, aren’t we just generating more artefacts that nobody maintains?”

The goal is not to create 50 MVCs in the first month, but to start small, reuse patterns, and eliminate redundancies.

If MVC becomes a cottage industry of templates, it’s a sign your operating model is wrong — not that the idea is.

The MVC playbook: Five Steps to Agent-Ready Context

Here’s a lightweight MVC playbook you can run with a CDO, business owner, risk, and product/AI lead in the same room.

The Ultimate MVC Framework Guide

Step 1 — Start with the Decision

Don’t inventory data; pick one real decision and map what the agent can touch.

Collections, “What should we do when a customer misses a payment?” Claims, “What’s the next step for a low-complexity incident?” IT ops, “How do we handle a recurring incident we’ve seen 50 times?

Write down: Use case/workflow — where the decision lives. Core decision(s) — in plain language. Process step — where the agent intervenes (before/after what). Boundaries — what the agent can decide vs what stays human.

It is where governance quietly starts: you define what the agent is allowed to touch.

Step 2 — Define Features + Outcomes

Clarify where AI adds value and which data each feature needs.

Decide how AI shows up in this flow:

• Retrieval and summarization?
• Planning and sequencing?
• Tool use and workflow triggers?
• Memory and personalization?
• Monitoring and anomaly detection?

For each feature, capture:

• Why you’re using it
• Data it depends on
• The business metric it should move

Example for collections: Retrieval → latest customer + account + contact history → fewer “blind” calls, better first-contact resolution Tool use → log promises, schedule follow-ups, send compliant SMS → less manual admin, faster time-to-arrangement Planning → choose between plan, escalate, pause → more successful arrangements, fewer unnecessary escalations

Governance can now approve something concrete: “This agent uses retrieval + tools + planning for this decision, on this data, for this outcome.”

Step 3 — Define the Minimum Viable Context

Now talk about data and policy together. “For this decision and these AI features, what is the minimum context our agent must trust to be safe and useful?”

Specify only essential entities, sources, freshness, and restrictions.

1. Entities & attributes

• e.g., Customer, Account, Loan, Interaction, Promise-to-Pay
• Critical attributes: balance, days past due, risk band, hardship flag, last contacts, last promise status, open complaints.

2. Sources & authority

• Core banking, collections system, CRM, contact center, logs
• “Source of truth” rules (e.g., collections platform wins for promise status).

3. Freshness & latency

• What must be real-time? (e.g., payment events, hardship flags).
• What can be daily / weekly? (e.g., risk band recalculation).

4. Sensitivity & restrictions

• Fields that are restricted, masked, or excluded in certain decisions.
• Attributes that must not be used (e.g., sensitive health codes, protected characteristics).

You’ve just moved from “our data is messy” to “These 10–20 fields, from these sources, with this freshness and restrictions, are non-negotiable for this agent.”

Step 4 — Make Governance Explicit

Turn this into a mini-governance artefact, assigning:

1. Ownership & sign-offs

• Business owner (decision)
• Data/AI owner (context)
• Risk / Compliance approver

2. Policy & regulation mapping

• Which conduct, privacy, and sector-specific rules apply?
• How to enforce them: filters, thresholds, approvals, and audit logging.

3. Top risks, mitigations, and kill-switches

• Risks: unaffordable plans, aggressive contact with vulnerable customers, wrong customer records, and silent bias.
• Guardrails: affordability rules, vulnerability flags, contact frequency limits, identity checks.
• Kill-switch criteria: when must the agent downgrade to “advise only” or be paused entirely? (e.g., policy change, anomaly threshold exceeded, new product type launched.)

4. Allowed vs forbidden use

• Segments and regions where the agent is allowed.
• Explicit out-of-scope cases (e.g., high-value disputes, regulatory investigations).

Every new MVC is governance work in context, not another abstract policy document.

Step 5 — Monitor and Evolve

Track KPIs, guardrails, and drift; refine or merge MVCs as patterns mature.

1. Success metrics

• Business KPIs (resolution rate, time-to-outcome, default rate, NPS, cost per case)
• AI KPIs (suggestion acceptance, overrides, errors, time saved)

2. Guardrails

• Complaints, policy breaches, odd decision patterns (e.g., an unexplained spike in “pause” recommendations), and bias indicators.
• Drift indicators (new product codes, new customer segments, policy changes).

3. Review cadence and triage

• Who reviews what, how often.
• What triggers a redesign of the MVC vs a simple parameter change
• When to roll back, pause, or expand.

4. Reuse & curation

• Which parts of this MVC are now standards (naming, risk bands, flags, logs)?
• Which MVCs can be merged as patterns converge (e.g., across retail products)?
• Which should be retired when workflows change.

Over time, your portfolio of MVCs becomes a library of patterns (not a pile of templates), and a map of where you are truly agent-ready.

Where MVC lives in your Operating Model

MVC only works if it has a home.

Data readiness for agents is a team sport

Viewing MVC solely as a “data thing” will lead to stagnation. Instead, see it as a collaborative artefact that bridges governance, change, and agent behavior.

SaaS and Vendor reality: MVC when you don’t own the Stack

A fair objection: “Half our critical workflows live inside SaaS platforms and vendor products. We don’t control their feature stores or context fabrics.”

You can still apply MVC thinking — it just turns into a minimum viable contract with your vendors.

For any agentic capability running in vendor tools, you should be able to ask:

• Which decisions does it actually make or support?
• Which entities and attributes does it rely on from our data?
• How fresh is that data, and what are the failure modes?
• What policy controls and guardrails exist — and which are configurable vs hard-coded?
• What logs and explanations can we access, and how quickly?

If a vendor cannot give you an MVC-equivalent view:

• You either treat that capability as advisory only, or
• you treat it as high-risk and compensate with stricter human checks.

MVC is as much about transparency and accountability as it is about architecture.

The Data Flywheel — with Regulators and Politics in the loop

Practitioners often describe the data flywheel as a key source of advantage — each interaction generates data that improves models and experience, which attracts more usage, which generates more data.

For agents, MVC and context fabrics are what make that flywheel safe and focused.

Think of flywheel as a four-step loop

In the real world, this flywheel is not free to spin however it likes:

• Regulators may require approvals for specific changes in policy or decision logic.
• Risk may cap the percentage of decisions that can be fully autonomous.
• Politics shows up when the flywheel suggests changes that hit P&L or specific customer segments in uncomfortable ways.

That’s why MVC needs:

• clear kill-switch criteria (when to slow or stop the agent),
• a governance forum that can approve not just “more accuracy” but “more risk, more reward”,
• and transparency so you can explain, after the fact, the reasoning behind a decision.

Field Story: from AI Theatre to Agent-ready Collections

Let’s revisit our mid-sized retail bank, but this time through the MVC lens.

Before: the “assistant” that NEVER really helped

The bank had all the usual ingredients:

• Modern data warehouse in the cloud,
• BI dashboards for risk and collections,
• A shiny new AI platform,
• Enthusiastic vendors.

On paper, they were “AI-ready.”

The first big bet was a “smart collections assistant” for call-center agents:

• Surface customer details,
• Suggest talking points,
• Propose payment plans.

In demos, it looked great. The assistant could:

• Summarize accounts,
• List missed payments,
• Pull policy snippets.

In production, it cracked:

• Suggested plans ignored recent promises-to-pay in another system,
• Vulnerable customers treated like everyone else,
• Agents distrusted the tool and overrode most suggestions,
• Risk and compliance couldn’t see which data fed which decisions.

They had built an AI façade on top of a Level 2 / early Level 3 environment: good analytics, fragmented context, no defined MVC. No real flywheel — just a demo loop.

They didn’t need more features. They needed clarity.

Step 1–3 in Practice: Decision, Features, Context

The CDO, Head of Collections, Risk, and Product lead locked themselves in a room with a whiteboard.

Decision and Role They wrote the decision in one sentence: “Given a missed payment on a retail loan or card, decide the next best action: propose a payment plan, escalate to a specialist, or pause contact.”

They clarified boundaries: The agent can: draft offers within ranges, recommend contact channels and timing, log notes, and schedule follow-ups. It cannot: write off debt, change interest rates, and override hardship and vulnerability policies.

AI features and Outcomes They chose three capabilities: Retrieval → combine customer, account, and recent contact history → fewer blind calls. Tool use → log promises, schedule follow-ups, send compliant messages → less admin, faster time-to-arrangement. Planning → choose between plan, escalate, pause within policy → more successful arrangements, fewer unnecessary escalations.

Minimum Viable Context They defined an MVC on one page: Entities: Customer, Account, Collections Case, Interaction, Promise-to-Pay. Attributes: balance and days past due, risk band, hardship flag, vulnerability indicators, last 5 contacts, last promise status, and open complaints. Sources: core banking (amounts, payments), collections platform (cases, promises, statuses), CRM, and contact-center (calls, messages, complaints). Sources of truth rules: collections platform wins for promise status; core banking wins for monetary amounts. Freshness: balances and payment events near real-time, risk band daily, and hardship flags immediately. Sensitivity & restrictions: hardship reasons and specific vulnerability codes masked; certain income and demographic attributes are excluded from the decision logic.

They implemented this as a Collections Context Fabric — a thin layer over existing systems, with views and APIs tuned for this MVC.

Step 4–5 in Practice: Governance, Change, Flywheel

They treated it as data, risk, and change work.

Governance Using the MVC as an artefact, they agreed: Owners and Sign-offs: Head of Collections — decision and metrics; CDO / Head of Data — context and quality; CRO / Compliance — policies, red lines, and kill-switches. Policy Mapping: Conduct rules and fair treatment for vulnerable customers; Privacy and use of sensitive data; Contact frequency limits. Kill-switch Criteria: Any spike in complaints linked to agent-initiated actions; Detected use of forbidden attributes; Anomalous patterns in decisions for specific segments.

Change and Incentives Call-centre teams helped define what a “good suggestion” looked like. Playbooks and scripts were updated for agent-assisted workflows. Team leaders had dashboards tracking suggestion quality and override reasons. Crucially, agents weren’t penalised for overriding — they were rewarded for spotting issues.

With decision, context, and guardrails clear, they finally had the foundation for a real data flywheel.

Spinning the Flywheel They launched in one region and segment, and instrumented everything: Business outcomes: time-to-arrangement, resolution rates, roll rates, and complaints. Agent Behavior: suggestion acceptance, overrides (and reasons), cases escalated. Risk Signals: policy breaches, vulnerability-based escalations, anomalous patterns (e.g., too many “pause” decisions).

All of it logged against the same MVC slice.

Monthly, the squad tuned prompts, thresholds, and “no-go” cases. Quarterly, the AI/data governance forum asked:

• Which parts of the context fabric can become standards (e.g., risk bands, hardship flags)?
• Which guardrails and kill-switch criteria generalize to other agentic decisions?
• Which new segments are ready for rollout?

Business Value:

• Agents readily accepted suggestions in simple cases, while overrides occurred more frequently in genuinely complex situations.
• Time-to-arrangement dropped for early-delinquency, low-risk customers.
• Complaints about “wrong person, wrong time, wrong offer” decreased in pilot segments.
• A new hardship code led to quick drift detection. As a result, the agent was temporarily downgraded to “advise only” until updates to the MVC were applied.

From there, reuse was natural. The same context, fabric, and governance approach seeded:

• a hardship detection agent,
• a branch advisor assistant,
• and early-warning outreach for customers under stress.

Same basic MVC playbook, different decisions. The bank now had a working data flywheel: real interactions → governed signals → updated logic and models → better interactions

Five Uncomfortable Questions for your following Steering Committee

You don’t need to be a bank — or at Level 4 — to start.

In your next steering meeting, skip the platform slide and ask:

1. **Which of our current “agents” are actually just Level-2 analytics with fancy UIs?
2. For any agent touching customers or money, can we put its Minimum Viable Context on one page — and show it to a regulator without sweating?
3. In the last quarter, when did our data or policy drift enough that an agent should have been paused — and did we notice?
4. Which vendors can (or cannot) give us an MVC-equivalent view of the decisions their tools take on our behalf?
5. Who owns our MVC library, what budget do they have, and which board-level metric does it move?**

If you can put tangible answers on a single page, you’re not just “doing agents.” You’re building an agent-ready data and governance muscle, one workflow at a time.

More than any new platform, that is the missing link between your data investment and agentic AI that you’re willing to defend — to customers, regulators, and your own people.

Please share your thoughts in the comments below. I’d love to hear and learn from you.

Don’t miss out on the upcoming episode of the AI Plugged Series — Episode #6: FINOps for AI. 🎉

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