Agentic AI in 2026: What Government Teams Actually Need
Federal agencies are moving past chatbots and into agentic AI that can perform real work - task routing, data prep, document actions, and decisions with oversight. Leaders at AWS, Cisco, and Oracle agree: the first step isn't the model. It's data organization and modernization.
That shift is already showing up in requirements. As Kapil Bakshi of Cisco put it, leaders are moving from "what is possible" to "what can we operationalize." The focus is on outcomes, not demos.
The First Mile: Structure Your Data
Agentic systems break without organized data. Rishi Bhaskar of AWS said it clearly: "Agentic AI is where our customers are headed… But that starts in the data journey."
That means defining authoritative data sources, cleaning and tagging records, and building access controls that respect policy and classification. Oracle's Peter Guerra calls it "context-aware AI" - or as he says, "AI that knows your data is the only useful AI out there."
Modernize the Plumbing
Agents need reliable pipes. Legacy code, brittle integrations, and siloed systems stall automation. Both Bhaskar and Bakshi emphasized updating infrastructure and code in parallel with data work.
Set up event-driven architectures where possible, standardize APIs, and ensure your logging, identity, and monitoring are consistent across environments. This is what makes agent decisions traceable and auditable.
Near-Term Workflows to Automate
- Network traffic management: triage events, route tasks, propose mitigation steps for human approval.
- Data entry: extract, validate, and post records across systems of record with audit trails.
- Document review: classify, summarize, and flag exceptions for contracts, grants, FOIA, and compliance.
Each of these aligns to clear metrics: cycle time, accuracy, case throughput, and cost to serve. Start where the data is available and rules are known.
Cloud Matters for Scale and Speed
Agentic AI is data-hungry and bursty. Guerra noted that cloud consumption gives teams flexibility to use what they need, when they need it - a fit for pilots that ramp into production. Bhaskar added that speed to cloud is speed to value; delays here ripple through every downstream dependency.
Stand up a secure landing zone early, define data movement patterns, and keep environments consistent across dev, test, and prod. It saves months later.
A Practical 90-Day Plan for Agencies
- Pick 1-2 workflows with measurable outcomes (e.g., reduce document processing time by 40%).
- Map data: systems of record, schemas, quality gaps, PII/SPII handling, lineage, and access policies.
- Stand up the stack: data pipelines, vector index or search, model gateway, event bus, and observability.
- Start with domain-specific models and retrieval; define evals for accuracy, bias, and latency.
- Put guardrails in place: human-in-the-loop checkpoints, audit logs, model cards, and red-teaming.
- Train the pilot team and write the SOPs before expanding beyond the first use case.
Governance You'll Be Asked About
Ground agent behavior in existing policy and risk guidance. If you haven't aligned to the NIST AI Risk Management Framework and current OMB guidance, make that part of your kickoff.
Team Skills: Make It Real
Agentic projects cross data engineering, security, model ops, and program delivery. Upskilling your core team early reduces rework and vendor drag later. Focus on retrieval workflows, evals, prompt safety, and monitoring.
If you need structured upskilling by role, see this overview of AI courses by job.
What This Means for 2026
Agentic AI will be judged by practical wins. Clean, well-governed data and modernized infrastructure are the difference between a pilot that stalls and a system that clears backlogs and frees staff from repetitive tasks.
Start small, make it measurable, and build the muscle to ship and operate these systems. The rest follows.
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