How we build agents that actually work
Every engagement follows the same disciplined methodology: discover the real cost, design guardrails first, build against live systems, then monitor and hand off. No shortcuts. No guesses.
Map the work. Quantify the cost.
The first step of every engagement is the same: we sit with your operations team and map the repetitive, rules-based task currently done by hand. Not a high-level description — the actual work. Every screen they touch, every decision tree they follow, every system they pull data from or write to.
We quantify the current cost: time per cycle, error rate, downstream rework, compliance risk. Then we identify every system the work touches — your marketplace, TMS, patient portal, CMS, whatever — so we know exactly what the agent will need to read and write.
This phase produces a written spec: what the agent will do, what it won't, and the baseline metrics we'll measure against when it goes live.
Define what the agent can do unsupervised.
Before we write a single line of code, we design the exact boundary between unsupervised work and human judgment. Not all tasks are the same, and neither are the guardrails.
Read-only mode
Agent pulls data, surfaces insights and alerts. No writes. Every finding goes to a human decision-maker.
Used in: Almeida & Cole (compliance monitoring). Lowest risk, highest friction.
Flag-and-approve
Agent identifies matches and prepares the action. Human reviews and clicks to execute. Audit trail captured for every decision.
Used in: Fenwick Logistics (order routing). Balances speed with oversight.
Full autonomy + rollback
Agent executes the full task unsupervised. Every action is logged; humans can inspect and revert within 24 hours.
Used in: Northwind Retail (inventory reconciliation). Only after discovery proves the rule set is bulletproof.
Every agent gets a real-time audit log. Every decision — what it read, what it matched, what it did or didn't do — is timestamped and immutable. That log lives in SiteCtrl Console and feeds into compliance reports without extra work from your team.
Code against live systems. Test against real data.
Build phase
The agent is built directly against your actual systems — your marketplace API, your TMS, your patient portal. Not a sandbox. Not a mock. Real credentials, real endpoints, real data flow.
Before it touches production, we test against 6–12 months of historical data. If the task currently touches 500 records a month, we replay 500 from your archive and watch what the agent does. If it would have made errors, we see them. If it beats the current baseline, we measure by how much.
Deployment includes a rollback path: the agent can be killed in 30 seconds if something goes sideways. We run that kill test in staging before go-live.
Live phase
The agent runs inside SiteCtrl Console alongside your team. On day one it sees real traffic. We watch the audit logs in real time and stand by for 72 hours in case a pattern emerges that needs correction.
After the first week, we compare the agent's actual performance against the baseline: time saved per cycle, error rate, downstream rework hours. If discovery said you'd save 40 hours a month and the agent saves 38, that's a win we can explain. If it's 20, we investigate.
Stay live. Measure. Choose your path forward.
The agent doesn't disappear after launch. It runs inside SiteCtrl Console with full visibility into what it's doing and why.
Retainer model
SiteCtrl.ai continues to run and monitor the agent. We iterate on rules, tune performance, and handle system changes on your behalf. You pay per month; we own the uptime.
Most customers choose this. You get leverage without distracting your team.
Hand-off model
After 90 days we hand the agent config and audit logs to your engineering team. You run it yourself against the SiteCtrl API. Full transparency; no lock-in.
Common for teams who want to own the infrastructure or integrate into their own orchestration.
Either way, you have the data. Every month we send a report tying the agent's work back to the baseline you set in discovery — hours saved, errors avoided, rework eliminated. No vanity metrics. Just the numbers you actually care about.
Ready to run the math on your operation?
Drop us a line. We'll walk through a discovery call, no pitch, no deck.