Agentic AI Development
Governed Agentic Systems
Mission impact
Agentic development changes the constraint your mission plans around: throughput. Work that queues for months — migrations, integrations, backlog remediation, documentation debt — moves in orchestrated, human-reviewed campaigns, so capability reaches the field on the mission's timeline rather than the staffing plan's. Because every agent operates under scoped credentials, audit logging, and human sign-off, that speed arrives without loosening your security posture or your accountability chain.
Agents that survive contact with production
Models are good enough to do real work. What still kills enterprise AI is the last mile: legacy systems, compliance review, ops handoff, and the absence of anyone who will own the agent after the pilot. Wilkes & Liberty builds agentic systems as forward-deployed engineers — discovery in the room, integration into the stack you already trust, governance on the path, and an operations handoff with named owners — so agents become force multipliers instead of orphan demos.
Force multiplication, not replacement: agents take repetitive, well-specified, high-certainty work; people keep architecture, judgment, and final review on irreversible actions.
One security bar for private and public clients
Private clients get the same security posture we design for government and highly regulated environments: least-privilege tools, human-in-the-loop authority, full auditability, and customer-boundary inference when the mission requires it. We do not run a “lite commercial” stack and a “hard federal” stack. Sector-specific control language and acquisition artifacts belong on the Federal Buyers path; the engineering bar is the product.
What we deliver
- Multi-step agent orchestration — plan, execute, and verify campaigns across systems and repos with human-reviewable checkpoints.
- MCP and agent-ready platforms — expose systems of record through the Model Context Protocol with explicit, revocable scopes.
- Governed tool access — policy between agents and infrastructure, tuned to your risk posture (see AI Governance & MCP Integration for the control plane itself).
- Customer-boundary inference — models and telemetry inside environments you control when required.
- Engineering and ops hygiene agents — keep trackers, PRs, docs, and systems of record synchronized with reality.
- Modernization acceleration — orchestrated, human-reviewed refactors and migrations that compress calendar time without lowering the bar. Pairs with Software Development.
Build vs fix — and vs govern
This page is about building new governed agents and workflows. Cleaning up broken or unreviewed AI-generated code is AI Remediation & Verification. Implementing connectors, redaction, and audit at the system of record is AI Governance & MCP Integration. Agents are non-person entities under a zero-trust reading of access control; the posture practice is Zero-Trust Architecture.
Governance first
- Least-privilege tools — scoped, revocable credentials; never blanket rights.
- Human-in-the-loop authority — agents propose; accountable humans approve merges, deploys, and publishes.
- Full auditability — actions attributable to agent, credential, and task on infrastructure you operate.
- Evals over demos — success criteria defined before orchestration expands; regression tests on prompts and agent configs under version control.
We run our own software practice on agent-orchestrated workflows and harden open governance components (mcp_sentinel, drupal-mcp-connector) on our estate. Those inspectable controls support customer implementations; identity, integration, evaluation, and operational ownership remain specific to the customer environment.
Related governance and private-AI platforms
Sentinel provides the related governance foundation for scoped agent access, policy, and audit. Helios names private model and retrieval environments delivered inside a customer-controlled boundary. Discovery determines which components fit, what must integrate with existing systems, and who operates the resulting service.
Frameworks (sector-neutral)
Delivery maps cleanly to NIST AI RMF functions (govern, map, measure, manage) and to zero-trust treatment of agents as untrusted principals. Dense federal memorandum mappings and agency-specific artifacts are collected on the Federal Buyers path rather than inline here — commercial buyers get the same controls without the acronym soup.
Engagement path
Unsure where agents create safe value? Start with the AI & Sovereignty Readiness Assessment. Ready to build? Open a ticket — structured intake (problem, systems, constraints, desired outcome) lands in our private queue and triages into assessment, implementation, or managed ops. Need ongoing model ops and agent engineering inside your boundary? That is managed ops.
Where an agent earns its place
A useful starting point is a bounded workflow with a named product, operations, or engineering owner—one where an agent can gather context, use tools, and prepare or execute work under policy. The timing matters: after the process and accountable owner are known, before a pilot receives production credentials, when a prototype cannot survive real permissions and failure modes, or when repetitive work is structured enough to automate without hiding judgment.
From bounded workflow to governed operation
One workflow is traced end to end: model and tool interfaces, system connectors, retrieval context, policy gates, evaluation, observability, and the operator experience. We establish the manual and risk baseline, build a constrained vertical slice, evaluate success and failure cases, integrate it into production controls, and hand off runbooks with a measured expansion backlog.
What is ready at handoff
- A deployed or production-ready agent workflow with named operator ownership.
- Tool contracts, access policy, evaluation cases, telemetry, and failure handling.
- Acceptance evidence, operating procedures, and a decision on what should—or should not—be automated next.
Sovereignty features
Agents run entirely on customer-controlled infrastructure: models, orchestration, and telemetry all inside your accreditation boundary. Tool access is granted through scoped credentials you issue and revoke; audit logs are generated and retained on systems you hold, available to your auditors without anyone else's permission. There is no dependence on a third-party agent cloud in the trust path, and the full workflow operates air-gapped where the environment requires it.
Defense & government relevance
For defense and federal buyers, agentic systems are governed AI systems first. Our delivery practice maps to the NIST AI Risk Management Framework (AI RMF 1.0) across its govern, map, measure, and manage functions, and supports the inventory, risk-determination, and human-oversight expectations of the OMB AI governance memoranda in the M-24-10 lineage. Agents are treated as non-person entities under NIST SP 800-207 — authenticated, least-privileged, and continuously evaluated. Every agent action is logged and attributable, irreversible actions are reserved for accountable humans, and all telemetry remains in customer-controlled infrastructure.