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Executive Summary: Agentic AI Training Data
With a focus on which use agentic AI case, which vendor, and which oversight committee, it is easy to overlook a key question – whether the data their agents will rely on is fit for autonomous use.
Agents do not read data; they act on it, and small defects compound at every step.
Over 24 months, we believe two cohorts of firms will emerge: those whose data is Structured, Current, Authoritative, Rich, Verifiable, and Symmetrical – the six SCARVeS© characteristics of good agentic AI training data – and that will absorb the consequences of deploying agents on unchanged data estates.
The obstacles between are not technical but governance ones and, as yet, not every firm has the capability to overcome them.
If you are scoping your first medium or high-risk agentic workflow and need a fast read on whether your agentic AI training data is fit for autonomous use, check out our Agentic AI Training Data Readiness Diagnostic.
Introduction
When scoping your agentic transformation programme, it is easy to overlook a vital question. Boards might ask which use cases to pilot, which vendor to choose, which committee should govern the agents, and how. Without doubt, these are essential questions, but they sit on top of a more fundamental one: where do you have sufficient training data for autonomous AI agents to rely on and, crucially for medium and high-risk agents, is it fit-for-purpose? Get that wrong and your answer to all the other questions may inherit problems.
Will your data compound accuracy or inaccuracy?
Agentic AI is not simply generative AI with additional orchestration. A generative assistant produces an answer; an agent takes actions – reading systems, calling tools, updating records, placing instructions – across many steps, with limited human oversight in between.
The data an agent draws on at runtime is therefore no longer just a passive reference; it becomes an operational input into autonomous AI behaviour. In practice, for many agentic workflows, this operational runtime data becomes part of the effective training environment. For firms buying agents from vendors rather than building them, this operational data layer is where much of the operational leverage for AI agents sits.
It is also where much of the risk sits. The UK Information Commissioner’s Office, in its January 2026 Tech Futures report on agentic AI, observed that inaccurate information can cascade across tools, databases and other agents, and that the complexity of agentic data flows makes accuracy obligations harder to satisfy than in any prior generation of AI.
Anthropic, writing about its own agent deployments, issued a subtly different warning that agent autonomy brings the potential for compounding errors. Small data defects can scale rapidly – and the data estate that worked well enough for human-mediated processes may become actively hazardous when an autonomous system reads from it.
The firms most likely to succeed over the next 24 months will be those whose data is fit for autonomous use
Two broad cohorts of mid-sized asset managers are likely to emerge.
The first will have made their operational data accurate, complete, current, relevant and accessible to autonomous systems. Their agents will be trusted with reconciliation exceptions, reference-data stewardship and first-line compliance checks at a standard the firm can evidence to supervisors.
The second will have deployed agents on top of unchanged data estates and may spend years absorbing the consequences – unnoticed errors, mishandled corporate actions, drift between agent behaviour and current policy, and a growing risk exposure to decisions no-one can reconstruct.
Five data-quality attributes will separate winners from losers in agentic AI:
- Accuracy – does the data reflect reality at the moment of reading.
- Completeness – does the agent see the whole picture or only what was digitized.
- Recency – is content current or six months superseded.
- Relevance – is the dataset curated to the task or whatever was on the shared drive.
- Availability – can the agent reach it in a controlled, auditable way.
Firms that score well will compound their advantage; firms that do not will accrue operational and regulatory risk.
SCARVeS© – the 6 Characteristics of Good Agentic AI Training Data
Good training data for an agentic AI workflow is not simply clean data, it displays SCARVeS© – the 6 characteristics of good agentic ai training data:
- Structured, with metadata that lets the agent retrieve the right thing in the right context.
- Current, because an agent relying on superseded policy may act inconsistently with current policy.
- Authoritative – it includes expert provenance, because the implicit judgement of subject-matter experts helps an agent distinguish between routine execution from escalation conditions.
- Rich – it should capture process as well as outcome, so it can inform reasoning chains, explain the importance of boundaries, and describe the conditions under which an experienced human would have paused or escalated.
- Verifiable against ground truth, not just plausible-looking.
- Symmetrical – covering both positive and negative outcomes, it should represent failure modes as well as the ideal scenario, for example, malformed vendor feeds, partial fills, or reference data contradicting itself at quarter-end.
We believe the firms whose training data scores well against the SCARVeS© benchmark will be the ones who join the first cohort because their data will be fit for autonomous use.
Overcoming The Obstacles
Four obstacles can stand between a mid-sized asset manager and agent-ready data, but only one is primarily technical.
- First, institutional knowledge is trapped in operational silos – email, SharePoint, vendor platforms, individual heads – and was never curated as an asset.
- Second, there is no governance structure for deciding what data the agent may train on, retain, and act from.
- Third, many firms do not yet possess the specialist capability needed to curate and label agent-relevant data.
- Fourth, the firm cannot tell whether the resulting estate is good enough not just to work, but to withstand supervisory challenge under Consumer Duty, the EU AI Act, or internal model risk management (MRM) standards.
Some of these your firm can solve alone or with existing partners – data engineering vendors and internal data teams can address the silo problem – addressing any capability gaps through hiring, training, and adapting existing data stewardship roles.
But the second and fourth obstacles – governance designed for autonomous data consumers, and demonstrating regulator-grade defensibility – are harder because they require capabilities most firms do not yet have in any function – and which a firm doing this alone will need to build before its first material agent goes live.
Governance for Autonomous Data Consumers
This is not the same as governance for data at rest.
The firm needs an AI agent governance policy specifying what an agent may read, write to, and act on; named owners for each data domain; version control so the agent always reads current policy while the prior version stays auditable; and decision-logging that captures the data the agent relied on to support AI agent auditability, not just what it did.
This work cuts across the AI or transformation function (which brings the agent expertise) and operational risk and compliance (which bring the documentation discipline). Few firms have both, and someone has to own the seam.
Regulator-Grade Defensibility
This requires a different capability: translating between three vocabularies – agent behaviour, internal risk policy, and external regulatory expectations such as Consumer Duty, EU AI Act Chapter III, and the MRM principles in PRA SS1/23.
A strong second-line MRM function can build half of this; the agent-behaviour-to-control mapping is the part most second lines have not yet developed.
Without it, a firm can have working agents and good data and still struggle to demonstrate regulator-grade evidence to supervisors.
Implementation Considerations
For firms building this themselves, the practical sequence is to appoint one accountable owner across AI / transformation and second-line risk, convene a small working group spanning both, and draft the governance policy and mapping artefact before the first material agent goes live – not after. The cost is mostly elapsed time and cross-function coordination, both routinely underestimated.
Our Agentic AI Training Data Readiness Service
For firms that conclude building their own medium and high-risk agents is the right path, the guidance above will help you navigate the journey.
If you want to move faster, you may want to engage an independent specialist. If so, check out our Agentic AI Training Data Readiness Diagnostic, which is a focused 2-day diagnostic audit that will tell you whether you have the data you need for your planned agent.
The service will score your firm against the six characteristics of good training data (SCARVeS), produce a readiness scoresheet, identify the top 3–5 risks, and indicate any regulatory exposures under Consumer Duty, EU AI Act Article 10, and PRA SS1/23.
If you are scoping your first medium or high-risk agentic workflow and need a fast read on whether your agentic AI training data is fit for autonomous use before committing to build, book a free consultation with us so we can evaluate whether the Agentic AI Training Data Readiness Diagnostic is right for your situation.
Frequently Asked Questions About Agentic AI Training Data
Agentic AI Training Data is the operational, procedural, and governance data that enables autonomous AI agents to perform tasks safely and reliably. Unlike traditional AI systems that only generate outputs, agentic systems may take actions across workflows, making data quality, recency, provenance, and auditability significantly more important.
Traditional AI systems typically generate recommendations or content for human review. Agentic AI systems may instead read systems, call tools, update records, and execute actions autonomously. As a result, inaccurate or outdated data can compound across workflows much faster and with less human intervention.
Good agentic AI training data should be:
- Structured.
- Current.
- Authoritative.
- Rich in operational context.
- Verifiable against ground truth.
- Symmetrical across both successful and failed outcomes.
Remember the acronym SCARVeS, because these 6 characteristics help determine whether data is fit for autonomous use.
Agentic AI systems rely on operational data to make or influence decisions autonomously. Firms therefore need governance controls defining what agents may read, write to, retain, and act on, alongside version control, audit trails, and evidence showing how decisions were reached.
