Agentic Systems & Multi-Agent Orchestration
Agents that plan, reason, and take real action across your tools — intake to resolution, with judgment steps automated and humans on the edge cases. In production, not in a demo.
Every vendor deck in your inbox promises agents. Almost none of them survive the trip from demo to production, because the demo skips everything hard: state management when step three times out, permissions when the agent touches the ERP, escalation when confidence drops, and the eval harness that proves this week's system is better than last week's.
We build agentic systems as what they are — distributed systems with a model in the loop. Specialized agents that hand off to each other, verify each other's work, and escalate to humans exactly where risk lives. Orchestrated, checkpointed, observable, and governed. That's the difference between an agent that demos and an agent that works Mondays.
Pick the workflow that pays
High volume, clear success criteria, tolerable failure cost. We find it in week one — it's usually not the one the steering committee guessed.
Design the system, not the prompt
Agent boundaries, tool contracts, state management, and human gates designed before the model is asked to be clever.
Ship against real traffic
Shadow mode first, then partial traffic, then autonomy earned by eval data — the safe path to real workload.
Instrument the ROI
Cases per day, cost per case, escalation rate — live on a dashboard, so 'is it working' has a number.
The workflows RPA couldn't touch — document-heavy, decision-dependent — handled end to end.
Agents act with scoped permissions, full audit trails, and human gates on the risky 5%.
Eval-gated quality and per-case cost telemetry from day one.
Which workflows are actually ready for agents?
Document-heavy, high-volume, judgment-dependent processes with clear success criteria: claims triage, support resolution, contract review, application processing. If skilled people spend hours a day on it and the failure cost of a caught mistake is tolerable, it's a candidate.
How do you keep an agent from going off the rails?
Architecture, not hope: scoped tool permissions enforced in code, confidence-based human gates, idempotent actions with audit trails, and eval suites that catch quality drift. The model proposes; governed infrastructure disposes.
Single agent or multi-agent?
Whichever the workflow demands — and we'll tell you honestly. Multi-agent earns its complexity when there are genuinely different jobs (routing, retrieval, judgment, escalation) benefiting from isolation and cross-verification. Sometimes one well-tooled agent is the right answer.
Ready to skip the kickoff theater and ship?
Tell us about the AI initiative your last three vendors couldn’t close. We’ll scope the outcome on a short call.
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