Agentic AI engineering
Build agents your business can scale, govern, and trust.
AI fails at the last mile. We engineer for the last mile.
What governed agentic execution requires
Trustworthy execution
Governed decisions with built-in approvals, controls, and auditability, so execution is defensible at scale.
Codified decision logic
Rules, thresholds, and exceptions defined explicitly, so humans and agents act on the same playbook.
Human and agent workforce
Agents operate within approved boundaries. Humans handle judgment, escalation, and exceptions.
Continuous optimization
Telemetry and outcomes feed an improvement flywheel that makes decisions better over time.
What we deliver
Agentic AI build
Custom agent development on Copilot Studio, Azure AI Foundry, or custom platforms, with governance and security configured up-front.
Agentic AI governance and readiness
Observability, security, and governance for agentic AI at scale, anchored on Agent 365.
Build-a-Bot workshop
A half-day session that moves your team from AI awareness to hands-on agent development. You’ll leave with a prioritized agent roadmap and a governance framework.
Decision infrastructure
Documentation and codification of complex, SME-driven business processes. Used as input to data platform modernization and AI solution scoping.
Illustrative agent patterns
Submission Readiness Agent
Guides users before submission. Directionally 20 to 40% fewer low-quality submissions.
Learning & Enabling Agent
Identifies repeat mistakes and converts patterns into guidance.
Offer Workflow Agent
Applies offer rules and confidence thresholds. Auto-routes standard cases. Escalates exceptions.
Operations Control Agent
Monitors aging work and queue pressure. Triggers proactive nudges.
How agentic operating models scale
1
Decision logic becomes explicit. Tribal knowledge is translated into rules, thresholds, and exceptions.
2
Decisioning becomes governed. Logic is codified and centrally managed. Exception paths are defined. The operating model becomes agent-ready.
3
Execution scales across humans and agents. Agents operate within defined decision boundaries. Humans focus on judgment, oversight, and exceptions.
4
Continuous optimization improves trust and outcomes. Feedback loops make decisions better over time. Improvement becomes continuous.
Governed Agentic Execution
Customer Zero
An agent we built, ran, and proved on our own work
Before we scale an agent into a client’s operating model, we prove the pattern ourselves. Inside a large global technology program, our program-management team ran a manually intensive, high-volume review process: validating work submissions generated by sellers and partners against a detailed set of rules, thresholds, and exceptions that lived in our SMEs’ heads.
We treated it as an agentic build, following the same approach we bring to client engagements
- Identify the right opportunity. We prioritized the most manual, time-intensive workflow, where AI would reduce friction, improve consistency, and increase scale.
- Codify the work. We translated the review logic our SMEs apply every day, the rules, thresholds, and exceptions, into decision infrastructure an agent could execute against, producing consistent, trusted outputs.
- Serve as Customer Zero. We incubated the agent inside the SME team, validating output quality, efficiency, and operational fit before scaling it anywhere.
- Operationalize and scale. We built the decision-infrastructure foundation, so the same pattern can extend to the next ten-plus review types without starting over.
What it demonstrates
- Quality and trustworthiness. Accurate, consistent outputs aligned to the review logic, with no material hallucinations, validating the decision-infrastructure foundation underneath.
- A continuous-learning loop. Reviewers validate results and feed edge cases back; that feedback is captured and systematized to refine the rules over time.
- A knowledge layer. The agent doubles as a queryable knowledge source, so reviewers can check criteria, understand failure conditions, and onboard without relying on tribal knowledge.
- Reusable by design. Because the decision infrastructure is standardized, the framework scales to new use cases by adapting rules rather than rebuilding.
Why Inviso for agentic AI engineering
Governance and security configured up-front, from the first build, not retrofitted after agents proliferate.
Agent 365 readiness depth on the governance side, where enterprise risk concentrates.
Decision infrastructure discipline. We codify the decision logic before we automate it, so agents inherit a playbook rather than improvising one.
Have a process still running entirely on human judgment? That’s an agent candidate.
FAQs
What is agentic AI?
Agentic AI is digital labor: AI agents that execute multi-step work within defined boundaries, rather than just answering questions. Agents scale routine execution by orders of magnitude beyond what a human workforce alone can deliver, while humans keep judgment, oversight, and exceptions.
Which platforms do you build agents on?
Copilot Studio for embedded process agents, Azure AI Foundry for multi-modal or custom-orchestrated agents, and custom platforms where the use case requires it. Every build pairs the agent with governance and security from day one.
What is the Build-a-Bot Workshop?
A half-day session that moves your team from AI awareness to hands-on agent development. You leave with a prioritized agent roadmap and a governance framework. A concrete starting point, not a slide deck.
How do you keep agents from going off the rails?
By engineering the boundaries before the agent exists: codified decision logic, policy thresholds, human review for sensitive decisions, and observability and audit trails through Agent 365. Governance is part of the build, not a policy document beside it.