Automation & AI Agents
Overview
Give every team a tireless, accountable digital workforce.
Repetitive, rule-based work is a drain on your team’s time, attention, and morale. Our Automation & AI Agents practice designs and deploys intelligent agents and end-to-end workflow automations that take over the work your people shouldn’t be doing, freeing capacity for the high-value, judgment-intensive tasks that actually require human expertise.
We go beyond traditional RPA. Using modern agent frameworks, LangGraph, CrewAI, AutoGen, Semantic Kernel, we build AI agents that can reason, use tools, maintain context, and collaborate in multi-agent pipelines. These are not fragile bots that break when a screen changes; they are adaptive digital workers that handle complexity, escalate to humans when stakes are high, and get measurably faster as they learn.
Why choose our Automation & AI Agents service?
Automation projects fail for predictable reasons: they start in IT, miss the process owners, automate broken processes, and deploy bots with no monitoring. UXBERT’s approach starts with the business, mapping real workflows, measuring real throughput, and designing automations that are built to be trusted by the people who depend on them.
We bring a full-stack perspective. Our team combines process engineering expertise with LLM engineering, API integration, and enterprise architecture, so we can connect your agents to legacy systems via hybrid RPA and LLM, to modern platforms via REST APIs and MCP, and to each other in sophisticated multi-agent orchestrations.
Every automation we build includes human-in-the-loop controls for high-stakes decisions, an agent evaluation framework measuring accuracy, cost, latency, and safety, and a production monitoring dashboard that gives your operations team full visibility from day one.
Discover more about our digital services get to know our expert team.
Our Process
Process Discovery & Opportunity Mapping
Task mining and structured process interviews across Finance, HR, Operations, Sales, and IT. Every candidate workflow scored on volume, complexity, error rate, and ROI potential, producing a Process Automation Heatmap and BPMN diagrams of current-state processes so stakeholders can see precisely what will change.
Agent Design & Architecture
For each prioritized workflow: agent architecture design selecting the right pattern (ReAct, Tool-use, multi-agent) and framework (LangGraph, CrewAI, AutoGen, Semantic Kernel). Agent tools, memory, knowledge sources, action scope, and human-in-the-loop intervention points defined before development begins.
Build, Test & Sandbox Pilot
Development in a sandboxed environment with a measured baseline establishing pre-automation throughput, error rate, and unit cost. Automated evaluation across accuracy, cost, latency, and safety dimensions. Pilot runs against real data in controlled environment before any production deployment is approved.
Production Deployment & Monitoring
Production agents deployed with full monitoring: Productivity Gain Dashboard tracking hours saved, cost reduction, and throughput against baseline. Alert thresholds for accuracy drift or cost spikes. Defined runbook for agent maintenance, prompt updates, and tool catalog management. Handover documentation for independent operation.
FREQUENTLY ASKED QUESTIONS
What kinds of workflows are best suited to AI agents?
How do AI agents differ from traditional RPA bots?
How do you ensure agents don't make high-stakes decisions without human oversight?
What frameworks and tools do you work with?
What does ongoing support look like after deployment?
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