Agentic AI, held to a higher standard.

Agentic AI Development

Autonomous agents that plan, execute, and hand off multi-step work — not single-turn chatbots.

We build agentic AI the way we'd want it built for our own agency: with enough autonomy to actually save time, and enough restraint to survive an audit. That balance looks different depending on who's asking. An engineer wants clean tool-use and predictable failure modes. A program manager wants assurance the agent won't quietly make a bad call on their behalf. A compliance officer wants a record of every action an assessor could question. A commercial buyer just wants it faster than the manual process it replaces. We design for all four at once — an agentic system that only satisfies the engineer rarely survives contact with the rest of the organization.

In practice, that means multi-agent orchestration and tool-use architectures that can actually break a task apart, retrieval and memory systems grounded in your source data rather than a model's general knowledge, and human-in-the-loop controls that step in before a high-stakes action goes out, not after.

Not every pattern carries the same weight. In our experience, Orchestrator–Worker and Autonomous Agents are the two most likely to change how a program actually runs — they're also the two with the least room for error if you build them wrong. The other four are workhorses: less dramatic, just as easy to get wrong in production. Expand any pattern below for the full picture.

A closer look — where each pattern helps, where it breaks, and who runs into it

Prompt ChainingAudit-Friendly View Details Hide Details +

Pros

• Easy to reason about and debug — each step is independently testable
• Every intermediate output is inspectable, which makes for a clean audit trail
• Simpler models can often handle individual steps, lowering cost

Cons

• Errors compound silently across steps
• Total latency stacks up — each step waits on the last
• No single step "owns" catching an upstream mistake

Workarounds

• Add a validation checkpoint after each step, not just at the end
• Log every intermediate output for replay and audit
• Set confidence thresholds that route to human review before the next step runs

Real World Customers

Government Benefits Agencies Insurance Claims Processors Healthcare Intake Systems Immigration & Visa Processing Tax & Revenue Agencies

RoutingBuilt to Scale View Details Hide Details +

Pros

• Specialized handlers outperform one generalist model
• Scales easily — add a new handler without retraining the whole system
• Each handler's scope stays narrow, easier to secure and audit

Cons

• Misclassification sends work to the wrong handler with full confidence
• No fallback path when the classifier itself is wrong
• Routing errors are invisible until someone downstream notices

Workarounds

• Add a confidence threshold — low-confidence classifications route to a person, not a handler
• Build a default/catch-all handler for anything that doesn't cleanly classify
• Periodically audit routing decisions against ground truth

Real World Customers

Government Call Centers 311 / 811 Citizen Services Commercial Support Ops Healthcare Triage Lines IT Service Desks Legal Intake & Matter Routing

ParallelizationSpeed Advantage View Details Hide Details +

Pros

• Faster turnaround — independent checks run at once, not in sequence
• Each branch can use the model or tool best suited to it
• A failure in one branch doesn't have to block the others

Cons

• Concurrent branches can return conflicting results
• Aggregation logic has to arbitrate — and that logic can be wrong too
• Cost multiplies; every branch is a separate inference call

Workarounds

• Define an explicit tie-breaking rule before you need one, not after a conflict
• Surface disagreements to a human reviewer instead of silently picking a winner
• Cap concurrent branches to keep cost predictable

Real World Customers

Defense Contractors Financial Services Risk & Fraud Manufacturing Quality Assurance Pharmaceutical Regulatory Review Multi-Jurisdiction Legal Review

Orchestrator–WorkerGame Changer View Details Hide Details +

Pros

• Clean separation of concerns — the orchestrator plans, workers execute
• Easy to add or swap specialized workers without touching the plan
• One central point for oversight and logging

Cons

• The orchestrator is a single point of failure
• One bad delegation stalls every downstream worker
• Debugging means tracing both the plan and every worker's execution

Workarounds

• Add orchestrator health checks with automatic retry or re-plan logic
• Let workers flag a bad delegation back to the orchestrator instead of failing silently
• Version and test the orchestrator's planning logic like production code, not a prompt

Real World Customers

Federal Case Management VA & Disability Determination Healthcare Intake Coordination Enterprise Procurement Ops Logistics & Supply Chain Multi-Agency Emergency Response

Evaluator–OptimizerQuality Multiplier View Details Hide Details +

Pros

• Output quality improves with each pass against a clear rubric
• Strong fit for tasks with a measurable "good enough" bar
• Cuts down manual review cycles for straightforward revisions

Cons

• Iteration loops can run past their intended budget
• Cost and latency scale with however many passes it takes to satisfy the evaluator
• Risk of the optimizer gaming the rubric instead of genuinely improving

Workarounds

• Set a hard cap on iterations and escalate to a person past that point
• Track cost per iteration so a runaway loop is visible before it finishes
• Periodically audit whether rubric-passing outputs are actually good

Real World Customers

Government Grant & Contract Offices Marketing & Content Teams Financial Reporting & Disclosures Technical Documentation Teams Research Proposal Development

Autonomous AgentsGame Changer View Details Hide Details +

Pros

• Handles open-ended, multi-step work without a pre-written script
• Adapts in real time as conditions change mid-task
• Cuts manual handling for high-volume, well-bounded work

Cons

• Every tool call and memory write needs an audit trail, or you can't reconstruct what happened
• Harder to predict behavior in edge cases the design didn't anticipate
• The highest-stakes pattern for oversight, safety, and compliance risk

Workarounds

• Log every action and decision in enough detail for a third party to reconstruct it later
• Require human approval before irreversible or high-stakes actions execute
• Scope tool access tightly — an agent can't misuse a tool it was never given

Real World Customers

Defense & Intelligence Programs Critical Infrastructure & Energy Regulated Financial Operations Cybersecurity Incident Response Healthcare Administrative Ops

AI Implementation — Government & Commercial

Production deployment inside the constraints you actually operate under — air-gapped, GovCloud, or standard enterprise environments.

Deployment is where most AI initiatives stall — not because the model doesn't work, but because it doesn't fit how the organization actually operates. We build for the people who have to live with it: the IT team securing it inside an ATO boundary, the case worker who has to trust it enough to actually use it, the program manager who needs it live before the next budget cycle, and the executive who has to show what changed. Done right, it isn't a pilot that sits in a slide deck — it's a system your staff opens every day, inside the environment you already run, doing the parts of the job that used to eat the most hours.

That means deploying across GovCloud, on-prem, or commercial cloud without re-architecting around whichever one you're in, wiring the agent into the case management and data systems your team already relies on instead of asking them to switch tools, and treating change management and training as part of the build itself — not an afterthought after go-live.

CMMC Compliance Assistance

Assessment prep, control mapping, and documentation for CMMC Level 1–2 — built alongside your AI program, not bolted on after.

That covers gap assessments against NIST SP 800-171 practices, SSP and POA&M drafting and maintenance, and mock assessments ahead of your official C3PAO review — the same three things most programs scramble to pull together in the final weeks before an assessment, done here as part of the build instead of after it.