"Agentic" is already being stretched to mean everything and nothing. When every roadmap slide says agents, the hard part is defining bounded autonomy that leaders can trust — and operators can audit.
A practitioner's definition
"Agentic workflows" has become common in defense AI conversations because the Department is explicitly investing in data, analytics, and AI as a decision advantage — see the Data, Analytics, and AI Adoption Strategy (PDF) and the Chief Digital and Artificial Intelligence Office. Vendor decks then compress that mandate into a single word: agentic.
Let me offer a practitioner's view: what agentic workflows are, where they deliver real value in government, and where hype outpaces reality — grounded in the same Responsible AI posture the Department has published for years (ethical principles, RAI strategy and implementation pathway PDF).
What "agentic" actually means
An agentic workflow is a sequence of actions where an AI system can:
- Make decisions and take actions
- Adapt its approach based on outcomes
- Operate without a human directing each step
The key distinction from traditional automation: the agent has some autonomy in how it reaches the goal — not only whether it runs a fixed script.
| Dimension | Traditional automation | Agentic workflow |
|---|---|---|
| Flow | Fixed script or playbook | Goal-directed with adaptation |
| Decisions | Human at each branch | Agent handles branches within policy |
| Best when | Steps are identical | Patterns repeat but cases differ |
Where value is clearest today
High-volume, pattern-rich decisions
In government operations, value is clearest where decisions follow patterns but are not identical every time.
Service desk triage is a strong example: an agent reads a request, classifies it, checks knowledge bases, attempts resolution, and routes to a specialist when it cannot resolve — genuinely agentic, and it saves human time at scale. OMB’s M-24-10 pushes civilian agencies toward inventories, governance boards, and minimum practices for AI that affects rights and safety — the policy envelope that will eventually constrain how aggressively agents act without humans in the loop.
Document-heavy workflows
Agencies produce and consume enormous volumes of reports, memoranda, assessments, and correspondence. An agent that can ingest a document, extract findings, cross-reference existing knowledge, flag discrepancies, and produce a structured summary is doing work that would take a human analyst hours.
Where hype outpaces reality
The vision of AI agents making autonomous battlefield decisions is technically fascinating and practically premature. The trust infrastructure — technical, legal, ethical, cultural — does not yet exist for autonomous AI in life-or-death scenarios. Building that trust is a decade-long project, not a single procurement cycle.
For weapons-autonomy specifically, DOD policy has long treated human judgment and command oversight as design constraints — see DOD Directive 3000.09 (Autonomy in Weapon Systems; also indexed under DoD issuances). That directive is the clearest institutional signal that “agentic” on a slide is not “autonomous fires” in the field.
The near-term winning pattern
The organizations that extract the most value from agentic AI in the near term will deploy agents for the unglamorous work:
- Administrative burden
- Information processing
- Repetitive coordination
…that consumes skilled human time without requiring skilled human judgment on every step.
That is not a failure of ambition. It is smart deployment: free humans for work that truly requires human judgment; let agents handle the rest.
Takeaway
I have spent my career deploying this class of workflow automation at enterprise scale. The models have improved dramatically. The implementation challenge is unchanged: start with the workflow, measure outcomes, iterate relentlessly, and never deploy technology for its own sake.
Further reading
- NIST AI Risk Management Framework (AI RMF 1.0) — the Govern / Map / Measure / Manage lifecycle; use it to separate demo from production.
- NIST AI RMF Playbook — actionable controls language program offices can adopt without buying a buzzword.
- DoD Responsible AI Strategy and Implementation Pathway (PDF) — how the Department operationalizes traceability, reliability, and governance.
- DOD Directive 3000.09 (PDF) — autonomy in weapon systems; the policy counterweight to “fully autonomous agents” hype.
- OMB M-24-10 (PDF) and M-24-18 (PDF) — civilian governance and acquisition guardrails that increasingly shape industry offerings sold back into defense programs.