AI & Agentic Engineering
We Work This Way, So We Can Advise This Way
Most consultancies talk about the agent era. Our engineering organization already runs on it: agents write, test, and review code under human architects, every day, in production systems.
That experience is the product on this page. Agent-oriented development changes how software gets built, how teams are shaped, and what things cost. We have made that transition ourselves, kept what worked, and discarded what did not, including an early multi-agent orchestration system we retired when the evidence said a simpler model with strong guardrails wins. You get the lessons without paying for the detours.
Agent Systems
Design and build of production agent workflows on Claude and Salesforce: tool design, MCP servers, memory, and human handoff points.
Safety & Governance
Data masking between your systems and any model, permission scoping, audit trails, and evaluation gates that block bad releases.
Cost & Operations
Token economics modeled before launch, hard quotas enforced in code, and per-workflow cost reporting once agents are live.
The Receipts
The engineering behind Ariv Health, built by our team, shows what we mean by production AI:
- Nine production MCP servers exposing healthcare analytics to AI clients, including a remote server live in claude.ai today.
- An LLM service with structural daily quotas per user and tenant, making worst-case spend a design property instead of a hope.
- A masking layer between the data plane and every model call, with regression tests that prevent future changes from weakening it.
- Byte-identical calculation gates for actuarial formulas, because in regulated domains "close enough" is not a number.
The full story, architecture included, is in the Ariv Health case study.
How We Deliver
Inside PracticeVantage, agent-oriented development is not a service line we resell; it is how our own engineering runs day to day: plan approval before code, test-driven changes, guardrail hooks that block unsafe operations before they execute, and a human architect accountable for every merge. That operating experience, including the mistakes we made adopting it, is what we bring to yours.
What An Engagement Looks Like
- Readiness assessment: your workflows, data, and teams scored against what agent systems actually require.
- First production agent: one scoped workflow taken from selection to monitored production, establishing your pattern for the rest.
- Engineering enablement: your developers adopting agent-oriented development with the guardrails we use ourselves.
Get In Touch
Tell us where your Salesforce org and your AI ambitions stand today.