AI Agent Implementation
AI agents that take real work off your team's plate — reading documents, processing email, answering internal questions, drafting output — integrated into your systems, governed by your rules, and reviewed by your people.
What is AI agent implementation? It is the design, build, and deployment of AI software agents that perform specific operational tasks — for example, extracting data from invoices into an ERP, routing inbound email, answering staff questions from policy documents, or drafting proposals from CRM data. Unlike off-the-shelf chatbots, implemented agents are connected to an organization's actual systems and data, constrained by explicit rules, and designed with human review steps where judgment or risk requires it.
Who this is for
- Teams processing high volumes of documents, forms, or email
- Departments where skilled staff do repetitive lookup and re-keying work
- Leaders who tried a chatbot pilot that never reached production
- Organizations that need auditability, not black boxes
What this fixes
Invoice and form data entered by hand
Inbox triage consuming hours daily
Staff can't find answers buried in policies and docs
Proposals and reports drafted from scratch every time
Research and enrichment done manually
Pilots that never integrated with real systems
What's included
- Workflow selection and agent design — from Audit findings or a focused discovery
- Data and system integration: ERP, CRM, finance, document stores, email
- Guardrails: permissions, data boundaries, output constraints
- Human-in-the-loop review workflows
- Testing against real historical cases
- Team training and runbooks
- Production monitoring and tuning period
- Baseline-vs-actual performance report
How it works
Agent work runs on phases 2–4 of the Olyra 120: design and pilot on one well-chosen workflow, build and integrate production-grade, then deploy, measure against baseline, and extend to adjacent workflows. A first agent typically reaches supervised production inside 30–60 days.
Deliverables
Working agents in production · Integration documentation · Guardrail & escalation spec · Training materials · Baseline-vs-actual performance report.
Example agents
AP agent
Extracts invoice data, matches purchase orders, posts clean records, and queues exceptions for human review.
Intake agent
Reads inbound referrals, extracts structured fields, checks completeness, and routes cases with a summary attached.
Policy & SOP agent
Answers staff questions from your documented procedures — with citations, so answers can be verified.
Research & drafting agent
Builds structured briefs on inbound leads and drafts first-touch emails for rep approval.
Realistic outcomes
- Hours per week returned to skilled staff on the target workflow
- Faster cycle times on document-driven processes
- Fewer re-keying errors and cleaner downstream data
- An agent pattern your team can repeat on the next workflow
Agent implementation FAQ
Will agents act without human approval?
Only where you decide they should. Every agent is designed with explicit autonomy levels: some tasks are fully automated (low risk, reversible), others always queue for human review (payments, client communications, anything regulated). You set the line; the system enforces it and logs everything.
Which AI models and tools do you use?
We're vendor-neutral. The choice depends on your data sensitivity, existing stack, volume, and budget — sometimes a frontier model API, sometimes a smaller hosted model, often plain automation with no AI at all where that's the better tool. Recommendations come with reasoning and alternatives.
How do you keep our data safe?
Data boundaries are set in the design phase with your IT: what data an agent can read, where it can send output, what gets logged, and what may never leave your environment. We favor configurations where your data is not used to train third-party models, and we document the full data path for your review.
How long until an agent is live?
A first agent on a well-chosen workflow typically reaches supervised production inside 30–60 days, following the Olyra 120 sequence. Complex integrations (legacy ERPs, regulated review steps) extend that; we tell you before we start, not after.
Find out what AI can actually do for your operations.
Start with an AI Operations Audit — a fixed-scope diagnostic that maps your workflows, scores your AI readiness, and hands you a prioritized 120-day roadmap. If we don't find real opportunities, you'll know that too.
Request an AI Operations Audit → Prefer to talk first? Book a 30-minute intro call.