Agentic AI is entering tech stacks across federal agencies, and even a one-percent error can cascade into mission failure. Unlike generative models, these systems can plan, reason, and take autonomous action, enabling missions to run at unprecedented speed. For federal agencies, the benefits include accelerated analysis, streamlined operations, and enhanced decision-making.
The problem is that autonomy also means error amplification. Agentic AI does not just respond to prompts in a simple one-step process. The AI is engaging in complex multi-step processes, often interacting with different systems. As DeepMind CEO Demis Hassabis cautioned, “if your AI model has a 1% error rate and you plan over 5,000 steps, that 1% compounds like compound interest,” rendering outcomes effectively random. In settings such as defense, intelligence, or citizen services, small inaccuracies can snowball into operational failures.
If data is the input that drives agentic AI, then every weakness in data quality or governance becomes a multiplier of risk. Federal AI readiness doesn’t begin with the model, but rather with the data that powers it.
Agentic AI in Federal Agencies
Across the federal space, agentic AI is already being tested and deployed in domains that demand precision and trust.
Defense and Intelligence
Autonomous systems are optimizing logistics chains, analyzing open-source intelligence, and providing real-time decision support for mission planning. They can simulate outcomes, detect threats, and surface insights faster than human analysts ever could.
Civilian Agencies:
Agentic AI is modernizing citizen services, from automated case processing and benefits adjudication to predictive models that anticipate infrastructure failures or public health trends.
Cybersecurity and Surveillance:
Autonomous agents are monitoring networks, flagging anomalies, and executing rapid response actions, often without human intervention.
In all these contexts, agentic systems interact with massive, interconnected datasets that often span legacy infrastructure, classified networks, and unstructured archives. Their ability to reason and act effectively depends entirely on the quality and completeness of the data they consume.
The Compounding Error Effect
Agentic AI operates through chains of micro-decisions, each one building upon the last. That means a single inaccurate input, bias, or gap in knowledge compounds exponentially through the process.
In a controlled environment, those compounding effects may be measured and corrected. In the real world, they can’t. Take for example:
- A mislabeled intelligence file propagates through analysis systems, altering threat assessments across multiple agencies.
- A biased policy dataset produces skewed eligibility or enforcement outcomes across citizen services automation.
- A corrupted data source feeds into planning models, resulting in unrealistic mission recommendations.
These risks are structural, inherent to the way autonomous systems learn and operate. When data errors compound, even the most advanced AI can rapidly lose alignment with mission objectives.
How Poor Data Governance Amplifies Risk
The federal data ecosystem is vast and fragmented, spanning structured databases, unstructured content repositories, and a plethora of siloed systems. Agentic AI can pull from every source, meaning the weakest link can dictate overall reliability.
Poor data governance amplifies error in three key ways:
1. Unstructured Data Exposure
- Sensitive files, emails, and reports often enter model training or retrieval workflows without proper tagging or classification.
- Without governance at the unstructured layer, agencies risk exposing sensitive or classified information to systems not designed to handle it.
2. Shadow Agents and Model Drift
- When agentic systems train on unvetted data, they can develop new behaviors, biases, or objectives that deviate from their intended purpose.
- Without centralized oversight or audit trails, these “shadow agents” evolve beyond visibility or control.
3. Accountability Gaps
- As agents make more decisions independently, responsibility becomes harder to trace.
- Weak data lineage and metadata management obscure the “why” behind an AI’s actions, complicating compliance with OMB M-25-21, EO 14179, and other federal standards.
An agent trained on unreliable or ungoverned data not only makes bad decisions but also undermines mission confidence.
The Foundation of Mission-Ready AI
Federal agentic AI systems can only be as effective as the data foundation they’re built on. Unified governance, especially across all repositories of unstructured data, is the key to preventing compounding error and enabling mission-ready autonomy.
Three pillars define this foundation:
1. Data Integrity and Validation
- Continuous monitoring for errors, inconsistencies, and anomalies across training and retrieval data.
- Lineage tracking to trace how every dataset, document, or communication influences model outputs.
- Periodic audits to maintain confidence in both structured and unstructured data pipelines.
2. Controlled Access and Oversight
- Role-based access controls to limit who and what can interact with sensitive data.
- Human-in-the-loop checkpoints for agentic decision-making, ensuring accountability at crucial junctures.
- The ability to pause or reverse agentic operations when defects or ethical risks are detected.
3. Unified Policy for Unstructured Data
- Consistent tagging, classification, and retention standards applied to all files and communications.
- Alignment with federal mandates on privacy, records retention, and security classification.
- Centralized visibility across repositories to ensure no data source becomes a blind spot.
When agencies govern at the unstructured content layer where most mission intelligence lives, they can manage AI risk at its source.
Laying the Foundation
Every compounding error is a governance failure at its core, a sign that the data foundation wasn’t sound. Federal agencies that treat governance as mission infrastructure will gain both technological advantage and institutional credibility.
Agentic AI doesn’t fail because it lacks intelligence, it fails because it lacks integrity. And integrity begins with data.
Ready to lay the data foundation for mission-driven, trustworthy AI? See how ZL Tech helps agencies leverage their unstructured data.
