Domain agents that plan, price, communicate and resolve entire business workflows end-to-end — with humans in command of the decisions that matter and automation owning everything else.
Enterprise operations teams run on a combination of ERP rules, email threads, Slack pings, and institutional knowledge living in people's heads. The data exists — in Salesforce, in the booking system, in the accounting ledger — but orchestrating it into a coherent action requires reading context, resolving ambiguity, applying judgment, and following up. That is beyond the reach of any rules engine or scheduled script.
Three workflows illustrate the gap: a catering quote that requires interpreting a client's non-standard requirements, a financial reconciliation that must identify the reason a vendor payment doesn't match the PO, and a resource scheduling problem where three competing priorities need a recommendation, not just a report. All three require reasoning. None have been automated. Until now.
Every agentic operation begins with a trigger event — an email arrives, a form is submitted, a threshold is breached in the ERP, a Slack message matches a pattern. The event hits a routing layer that classifies intent, extracts structured entities, and dispatches to the appropriate agent workflow.
The critical design principle is stateful persistence. Every workflow checkpoint is written to a durable store (Postgres) before execution proceeds. If an agent pod restarts, a human takes 3 hours to approve an interrupt, or a downstream API is temporarily unavailable, the workflow resumes from the last committed state with no data loss and no duplicate actions.
RestroAI's catering quoting workflow processes hundreds of inquiry emails per week. Before agentic automation, each quote required a coordinator to read the email, look up venue availability, pull pricing from the menu database, apply client-specific discount rules, check for dietary constraint conflicts, draft a proposal, and send it. End-to-end: 2–4 hours per quote, often extending to next-day when queues backed up.
Production result: Quote response time from 2.3 hours → 11 minutes median. Coordinator capacity freed: 68%. Quote-to-booking conversion rate +22% (faster response wins more business). Zero pricing errors since deployment — the critic agent catches arithmetic mistakes the old process missed.
Financial reconciliation is one of the most time-consuming back-office functions in any enterprise: matching thousands of transactions across bank statements, vendor invoices, purchase orders, and ERP entries. When records disagree — and they regularly do — an analyst must trace the discrepancy across 3–5 systems to find the root cause.
| Exception type | Agent action | Auto-resolve rate |
|---|---|---|
| Amount mismatch ≤ $50 | Apply rounding tolerance, post adjustment journal | 97% |
| Missing PO reference | Query ERP by vendor + date window, fuzzy-match PO | 84% |
| Duplicate payment detected | Flag for human review, freeze second payment in transit | 0% (always human) |
| Vendor payment delayed | Retrieve contract terms, send payment status email to vendor | 91% |
| Currency conversion delta | Pull FX rate at transaction date, recompute, post FX adjustment | 88% |
Resource scheduling — assigning staff, equipment, or venues to time slots while respecting constraints (certifications, availability, geographic proximity, client preferences, labor law) — is a classic combinatorial optimization problem. Traditional solutions are either rule-based (fast but brittle) or optimization solvers (accurate but opaque). Agents bridge the gap: they reason through constraints, apply domain knowledge, and explain their recommendations.
Architecture: The scheduling agent uses a two-pass approach. Pass 1 is constraint propagation — the LLM iteratively eliminates infeasible assignments by checking each constraint via tool calls against HR, certification, and availability systems. Pass 2 is recommendation generation — from the feasible set, the agent scores options by preference (client request, travel minimization, staff workload balance) and presents the top-3 ranked options with reasoning.
The scheduler never writes to the calendar autonomously — it always surfaces recommendations and waits for a human or the supervisor agent to confirm. This is intentional: scheduling decisions carry downstream consequences (staff notification, customer confirmation, resource locking) that justify a final human approval step even after 90%+ of the work has been done by the agent.
Autonomous agents operating on production business data require layered safety mechanisms. Our operational agents are governed by a three-layer guardrail stack:
Override design: Any human can override any agent decision at any point. The override UI is intentionally conspicuous — the same Slack card with a red "Override" button. Override events are logged with a required reason field. Override rate in production: 3.1% — primarily used for exceptional client situations, never for routine corrections the agent handles correctly.