October 14, 2026
Raja writes about why fidelity, provenance, and simulation are becoming essential to understanding how AI agents behave in the real world.
Airlines don’t wait for an engine to fail mid-flight to discover whether a pilot can handle it. They manufacture the emergency.
Engine failures, extreme weather, instrument malfunctions, and unexpected conditions are rehearsed repeatedly in simulators. The objective isn’t to recreate the sky perfectly. It’s to reproduce the conditions that matter when something goes wrong.
Enterprise AI is approaching a similar moment.
For years, synthetic data has been treated primarily as training fuel. When real data was scarce, sensitive, or expensive, organisations generated more examples to help models learn.
But an AI agent processing refunds, updating financial records or coordinating enterprise workflows presents a different challenge. It isn’t enough to know whether the model has learned the right patterns. We need to understand how the entire system behaves when something unexpected happens.
The next frontier of synthetic data is not manufacturing more information. It is manufacturing the conditions under which AI must prove it can work.
Fidelity: A realistic dataset is not a realistic enterprise
There are two standards of fidelity worth separating.
- Statistical fidelity: Does synthetic data resemble real data in its distributions, correlations, and proportions?
- Behavioural fidelity: Does the synthetic environment respond like the real world when an agent interacts with it?
Consider a refund-processing agent.
You can generate thousands of realistic transactions with accurate purchase values, customer histories, and refund frequencies. That gives you statistically representative data.
But what happens when a customer changes their mind halfway through? When the payment system times out? When two records disagree about whether a refund has already been processed?
These are behavioural problems. The agent must make decisions based on how customers, systems, and policies respond to its actions.
This is where synthetic environments often fail: they are too clean.
Generated data tends towards typical patterns. Enterprise operations, however, are full of missing fields, duplicate records, contradictory instructions, and exceptions that occur at precisely the wrong moment.
A simulation that removes these complications may produce excellent evaluation scores while leaving an agent unprepared for production.
The objective is fit-for-purpose fidelity: reproducing the conditions that influence decisions and the situations in which errors become expensive.
Provenance: Can you defend the world you created?
Synthetic data may be artificial, but the evidence behind it cannot be.
Provenance establishes where synthetic scenarios came from, how they were generated, what assumptions were introduced, which privacy protections were applied, and which versions were used during testing.
This matters for three reasons:
- Trust: Synthetic records derived from real customer information can still expose sensitive details. Enterprises need evidence of how that information was protected.
- Reproducibility: If an agent fails in version three, teams must be able to replay the same scenario against version four to verify whether the problem was resolved.
- Contamination control: If generated data is reused across training and evaluation, an agent may perform well because it has effectively encountered the test before.
There is another complication. Increasingly, AI systems generate the synthetic users and scenarios used to evaluate other AI systems.
If both share similar assumptions and blind spots, evaluation can become an echo chamber.
A simulation must therefore remain grounded in real operational observations and be challenged by scenarios the agent has not repeatedly encountered during development.
Otherwise, enterprises risk producing highly reproducible evidence of the wrong thing.
The simulation layer: Building a flight simulator for enterprise AI
Fidelity and provenance become more powerful when combined into a persistent simulation environment.
Rather than creating synthetic datasets for individual experiments, enterprises can establish a dedicated layer where agents encounter realistic operating conditions before interacting with production systems.
That layer needs five components:
- Synthetic actors: Simulated customers, employees and users with different goals, behaviours and constraints, including adversarial behaviour.
- Synthetic systems: Representations of CRM platforms, payment systems and enterprise tools, including their failure modes.
- Scenario libraries: Versioned collections of routine situations, unusual exceptions and high-risk operational events.
- Evaluation: Measures whether agents complete work, respect policies, handle uncertainty, and achieve intended outcomes.
- Replay and comparison: Runs identical scenarios against different agent versions, models, or configurations to identify changes in behaviour.
The value lies in testing entire workflows rather than individual model responses.
A refund agent might interpret a policy correctly but issue a duplicate transaction after a system timeout. A service agent might escalate appropriately but fail to transfer the information a human needs to resolve the case.
Each individual step may appear successful. The end-to-end outcome may still be wrong.
What should an enterprise actually rehearse?
Six categories deserve particular attention:
- Routine operations at scale: Can the agent maintain performance without accumulating errors, delays, or excessive costs?
- Ambiguous requests: Does it clarify, escalate, or guess when information is incomplete?
- Adversarial users: Can manipulation push it beyond policy or expose protected information?
- System failures: Does it retry, stop, or recover safely when tools fail?
- Policy boundaries: Are approval limits, compliance requirements, and business rules consistently respected?
- Hand-offs: Does essential context survive when work moves between agents, systems and people?
These scenarios represent the conditions under which enterprise work succeeds or breaks down. They can be rehearsed without inconveniencing customers, corrupting records, or creating financial consequences.
When simulation becomes infrastructure
The immediate benefit is safer testing. The larger opportunity is changing how enterprises make decisions about AI.
Release decisions become evidence-based. Teams can establish measurable deployment criteria rather than relying on polished demonstrations.
Risk becomes observable. Legal, compliance, and operations teams can examine how agents behave under relevant conditions instead of debating hypothetical failures.
ROI becomes easier to estimate. Simulations reveal completion times, operating costs, failure rates, and human-intervention requirements.
Technology choices become comparable. Models and vendors can be evaluated against the enterprise’s own scenarios rather than relying exclusively on external benchmarks.
But simulation is never a guarantee.
Agents can become optimised for familiar scenarios. Business policies change. Synthetic users may fail to represent real customer behaviour. A test environment can gradually become a model of a business that no longer exists.
The discipline must therefore be continuous: refresh scenarios, maintain independent evaluation cases and feed real operational incidents back into the simulation.
Over time, the simulation layer becomes more than testing infrastructure. It becomes an evolving representation of the enterprise’s operational knowledge, including exceptions, dependencies and rules that were never formally documented.
Synthetic data began by helping AI learn from more examples. Its next role is helping enterprises understand how AI behaves when examples become actions, decisions, and consequences.
The real value of a flight simulator isn’t that the pilot lands successfully every time. It’s that you can discover what goes wrong without losing the aircraft.