Replace Legacy RPA with Modern Agentic Automation

Agentic automation uses AI agents that reason, adapt, and make decisions to handle business workflows, rather than rigid, rule-based scripts. Unlike traditional RPA, these systems interpret unstructured data, self-correct when conditions shift, and improve continuously through use, handling genuinely complex, judgment-heavy work end to end instead of breaking the moment a process changes.

RPA follows fixed, pre-programmed rules and breaks the instant a screen layout or process step changes; agentic automation reasons about the task and adapts. Where RPA is limited to structured data and requires extensive reprogramming for every exception, agentic systems handle unstructured inputs, self-heal around minor changes, and learn from every interaction rather than staying static.

No. We triage your existing automation into three buckets: keep what's working and stable, wrap brittle bots with an agentic layer that handles the exceptions they currently choke on, and replace only the automations that are genuinely too fragile to extend. Most clients keep the majority of their RPA investment and layer agentic capability around its weak points.

We build primarily on n8n and Make, open, extensible automation platforms, plus custom agent frameworks for cases that need bespoke reasoning logic. Open tooling means you're not locked into per-bot licensing fees the way many enterprise RPA vendors structure pricing, and you retain full visibility and control over every workflow we build.

Start with processes that are high-exception, span multiple systems, and require light judgment calls, exactly where traditional RPA struggles most. Examples include invoice exception handling, customer inquiry triage, and claims intake. We help you prioritize using the same readiness-and-impact framework from our AI Assessment, so the first agent you deploy is the one most likely to prove value fast.

Most engagements get a pilot agent into production within 4-8 weeks. Week 1 is assessment and planning, weeks 2-3 are platform selection and integration setup, weeks 4-6 build and test the pilot workflow, and weeks 7-8 deploy to production with monitoring in place before we hand over or move to the next use case.

Cost is structured around build, run, and oversight, not per-seat licensing like most legacy RPA platforms. You pay for the initial build of each agent workflow, ongoing hosting/run costs (often lower with open tooling like n8n), and a light oversight retainer for monitoring and tuning. We scope a fixed-price estimate after understanding your specific processes on a discovery call.

Every agent we build runs inside explicit guardrails: defined action boundaries, confidence thresholds, and human-in-the-loop checkpoints for anything high-risk or ambiguous. When an agent hits a case outside its boundaries, it escalates to a human instead of guessing, and every action is logged so a mistake can be traced, rolled back, and used to tighten the guardrail that let it through.

Yes. We integrate through whatever surface is available and most stable, REST/GraphQL APIs first, file-based exchange where that's the system's normal interface, and UI-level automation only when there's genuinely no other option. We've connected agents to common ERP, CRM, and legacy on-prem systems without requiring you to replace or upgrade the underlying platform.

No prior AI skills are required to operate what we build. We hand over documented workflows, an admin interface for monitoring and adjusting agents, and hands-on training for whoever will own the platform day to day, so your team can make small changes confidently without needing to understand the underlying models.

Data stays inside boundaries you approve: we scope exactly which systems an agent can read and write, avoid sending sensitive data to models or vendors that don't meet your compliance requirements, and offer self-hosted deployment options (via n8n) when data residency rules require it. Every integration is documented so your security team can audit exactly what an agent can touch.

We capture a baseline for the target process, processing time, error rate, cost, before building anything, then track the same metrics after deployment. Typical results across our engagements land at 60-80% faster processing, 90-95% accuracy, and 50-70% cost reduction, and we build a simple dashboard so you can watch the numbers move in real time, not just take our word for it.

Realistically, agentic automation redistributes tasks rather than eliminating roles outright: it takes over the repetitive, high-volume, rule-followable parts of a job and routes the judgment-heavy exceptions to your team. Most clients redeploy the freed-up time toward higher-value work rather than headcount reduction, though the honest answer depends on your specific process and how automatable it really is.

Agentic systems need ongoing monitoring for drift, model or API changes upstream, shifting input patterns, and periodic evaluation against real outcomes to confirm accuracy hasn't degraded. We offer an ongoing oversight retainer that covers this monitoring and tuning, or we can hand the platform fully to your team with documentation and training if you'd rather own it in-house.

Start with our AI Readiness Assessment to identify the highest-value, lowest-risk process to automate first, then run a small pilot on that single process with a parallel manual run for validation before expanding. This staged approach means you see real results and build internal confidence before committing further budget or replacing anything mission-critical.