Nexus vs UiPath: RPA Bots vs Autonomous Agents

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Quick honest summary

UiPath built the RPA industry. For over a decade, their software robots have helped enterprises automate predictable, screen-based tasks: copying data between applications, filling forms, processing structured documents. That work is real, and UiPath does it well.

But RPA is sturdy on the structured path and brittle on everything else. Most enterprises that deploy it discover the same thing: the automatable tasks they can actually automate represent a fraction of what they need. The processes with the highest business impact (the ones involving ambiguous inputs, exceptions, judgment calls, and multi-system coordination) remain manual because bots cannot interpret intent, hold a conversation, or make autonomous decisions. UiPath's own research acknowledges this: 90% of IT executives say they have business processes that would benefit from agentic AI, and the company itself is pivoting hard toward "agentic automation" with products like Agent Builder, Maestro, and Autopilot.

Nexus was built agent-first from the ground up. Not RPA with AI bolted on, but autonomous agents that understand business logic, reason through exceptions, and complete workflows end-to-end. Where RPA executes predefined scripts perfectly until something shifts, Nexus agents combine process execution with conversational intelligence and autonomous decision-making, replacing the human judgment that automation requires at every exception point. Combined with Forward Deployed Engineers who embed with your team, Nexus is a solution (platform plus service), not just software you purchase and figure out on your own.

The right choice depends on what you need to automate and where you are in your automation journey.

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Side-by-side comparison

Dimension	UiPath	Nexus
Core approach	Software robots that mimic human actions on screen Click buttons, copy data, follow scripted sequences Sturdy on the structured path, brittle on everything else Now adding AI capabilities (Agent Builder, Autopilot, Maestro) on top of RPA foundation	Autonomous AI agents on agent-first architecture Agents understand business logic and interpret intent Reason through exceptions, ambiguous inputs, and edge cases Combine process execution with conversational intelligence and autonomous decision-making
Architecture	RPA-first Screen-level automation enhanced with AI layers Foundation remains rule-based bots that execute predefined scripts AI features are additive, not foundational	Agent-first Intelligence is the foundation, not an add-on Agents reason about business logic, hold conversations, and make autonomous decisions No screen interaction replaying
How it handles exceptions	Bots stop on unexpected events (UI change, new data format, ambiguous input) Cannot interpret intent or make judgment calls Requires manual intervention or bot rebuilds at every exception point New "agentic" features aim to reduce this, but underlying architecture remains rule-based	Agents reason through exceptions, interpret intent, and make autonomous decisions Replace the human judgment that automation requires at every exception point Agent understands the purpose of the work, not just the sequence of clicks Escalate with full context when confidence is low
Who builds and owns it	Typically requires IT, RPA developers, or a dedicated Center of Excellence Autopilot aims to make this easier Complex automations still need technical resources	Business teams build and deploy agents directly No engineering dependency Forward Deployed Engineers support design, integration, and change management
Deployment model	Software platform Purchase licenses, then build and maintain bots Use your own team or hire partners Professional services available but separate	Solution: platform plus embedded service Forward Deployed Engineers from day one Change management and integration support Ongoing optimization included
Deployment speed	Weeks to months per bot Depends on process complexity Significant upfront process mapping required Bot development and testing needed	Days to weeks for production agents Orange deployed in 4 weeks Lambda deployed in days
Maintenance burden	High Bots break when applications update their UI, when data formats change, when edge cases appear This brittleness is structural: scripts that execute perfectly on the defined path shatter on any deviation Maintaining bots often costs more than the automation saves Investing in AI-powered self-healing, but screen-level fragility remains	Low Agents connect through APIs and understand business logic Interface changes do not break them Agent logic updates in hours, not weeks Lambda: "Every change meant starting over. The agent adapts."
Integrations	Primarily screen-level automation (interacts with application UIs) Growing library of native connectors Process mining and task mining for discovery	4,000+ API-level integrations CRMs, ERPs, communication tools, custom APIs Deploy across Slack, Teams, WhatsApp, email, phone, web
Pricing model	Credit-based "Unified Pricing" or per-bot licensing Platform fees on top Enterprise deals often six figures annually Costs scale with bots and AI credits consumed	Per-agent pricing tied to value delivered Every engagement starts with a 3-month POC Defined outcomes before commitment
Security and compliance	Enterprise-grade security SOC 2 and various compliance certifications AI Trust Layer for governance of agentic features	SOC 2 Type II, ISO 27001, ISO 42001, GDPR Full audit trails and decision traceability Role-based access Every agent decision logged and explainable
Best for	High-volume, fully predictable, screen-based automation Stable UIs required Legacy system automation where no API exists Organizations with dedicated RPA teams The structured path only	Complex enterprise workflows with exceptions, ambiguous inputs, and judgment calls Processes that need conversational intelligence and autonomous decision-making Multi-system coordination Forward Deployed Engineers and change management included

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When UiPath is the better choice

UiPath is the right choice in specific scenarios, and it is worth being straightforward about that. RPA is sturdy on the structured path, and if your work stays on that path, it delivers real value:

• Your processes are fully predictable with zero variation. If the work follows the exact same steps every single time, with no ambiguous inputs, no edge cases, no judgment calls, and no variation, UiPath handles this reliably. High-volume data entry, screen-to-screen transfers, and structured form processing are genuine RPA strengths. The key question: does the process ever require someone to interpret intent or make a decision? If not, RPA is a solid fit.

• You need to automate legacy systems with no API access. UiPath's screen-level automation is genuinely useful when the only way to interact with a system is through its UI. Old desktop applications, mainframes, terminal-based systems: if there is no API, RPA was designed for exactly this.

• You have a mature RPA Center of Excellence with dedicated resources. UiPath delivers strong results when organizations invest in maintaining it. Bots break when UIs change, when data formats shift, when new edge cases appear. If you have developers who build bots, testers who validate them, and an operations team that monitors and fixes the breakage, the platform works well for the right use cases.

• The applications you automate have stable UIs that rarely change. RPA's brittleness is directly tied to screen-level dependencies. If the applications you automate have interfaces that do not update frequently, bots run reliably with lower maintenance overhead.

• You are already deep in the UiPath ecosystem and your current automations are working. If you have years of investment in UiPath, a functioning RPA program, and your automations are delivering value for the processes they cover, there may not be a reason to change what is working. The question is whether those processes represent the full scope of what needs automation, or whether the highest-impact work is still manual because it involves too many exceptions for bots to handle.

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When Nexus is the better choice

Enterprises that partner with Nexus tend to share a specific pattern: they have tried RPA or workflow automation (tools like Zapier and Workato), seen it work for simple cases, and then hit a ceiling. RPA tools execute predefined scripts perfectly on the structured path, but they break on everything else: ambiguous inputs, exceptions, judgment calls, edge cases. They cannot hold a conversation, interpret intent, or make autonomous decisions. The processes with the highest business impact, the ones that actually move financial outcomes, involve too many exceptions, too much judgment, and too many systems for rule-based automation to handle. Agents combine process execution with conversational intelligence and autonomous decision-making, replacing the human judgment that automation requires at every exception point. That is where the real value lives.

• Your highest-value processes are still manual because they involve exceptions. Customer onboarding, support triage, compliance monitoring, sales research: these processes involve ambiguous inputs, unusual requests, and situations that require judgment rather than rules. UiPath bots stop when they encounter something unexpected because they cannot interpret intent or decide what to do next. Nexus agents reason about the exception and either handle it autonomously or escalate with full context.

• You are spending more time maintaining bots than the automation saves. This is the pattern enterprises with existing RPA deployments describe most often. A UI update breaks a bot. A new data format causes failures. A process change requires the bot to be rebuilt from scratch. Nexus agents operate at the business logic level, not the screen level, so they do not break when interfaces change.

• You want a partner, not just a platform. UiPath sells software. Nexus embeds Forward Deployed Engineers with your team: real engineers who help identify the highest-impact use cases, design agents for your specific workflows, handle integration complexity, and support change management. Deploying AI at scale is 10% technology and 90% organizational change. Nexus is built around that reality.

• Business teams need to own the automation, not wait for IT. Some organizations try to solve this with developer frameworks like LangChain or CrewAI, but those still require engineering teams. UiPath typically requires developers or an RPA Center of Excellence. Business teams identify what they want automated, then wait in a queue while IT builds and maintains it. Nexus agents are built by the business teams who understand the workflows. Lambda's sales intelligence agent was built by their Head of Sales Intelligence, not an engineer. Orange's agents were built by their business team, not IT.

• Your processes span conversations, judgment, and multi-system coordination. RPA automates screen actions. It clicks buttons and moves data. It cannot have a conversation with a customer, interpret what they actually need, reason about whether an account meets compliance requirements, or decide which team should handle an escalation. These are the judgment calls that automation tools require a human for at every exception point. Nexus agents handle the full scope: conversational intelligence, autonomous decision-making, business logic, and workflow execution in a single system.

• You need governance woven into how agents work, not layered on after. Nexus agents are built with decision transparency, data traceability, and audit trails by design. Every decision is traceable: what data informed it, which rules applied, why it escalated or approved. At Orange, when the agent confidently approves, it approves. When uncertain, it escalates to the salesperson with full context. 100% adoption, 100% compliance. Not heavy-handed controls; governance woven into the work itself.

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What enterprises experienced

Orange Group: tried automation, chose agents

Orange automated 120 business processes with rule-based automation tools before transitioning to Nexus agents for the high-value work that RPA could not handle. As a multi-billion euro telecom with 120,000+ employees, they had every option available: internal engineering, RPA tools, enterprise platforms, external agencies. Their business team (not engineering) built autonomous customer onboarding agents using Nexus. Deployed in 4 weeks. 50% conversion improvement. $4M+ incremental yearly revenue.

The difference from their automation experience: automation tools executed the structured path perfectly but broke on everything else. When a customer's situation does not fit the standard flow (ambiguous requirements, system compatibility issues, edge cases that need judgment), the agent interprets the intent, reasons about the exception, and either adapts or escalates with full context. An RPA bot would stop and wait for a human to intervene. The agent replaces the human judgment that automation required at every exception point.

Every step is visible. Every decision is logged. A dashboard shows all interactions. Result: 100% adoption and full compliance. Sales teams own the outcome.

Lambda: automation was too rigid, AI assistants too inconsistent

Lambda, a multi-billion dollar AI infrastructure company with $500M+ ARR, tried traditional automation before transitioning to Nexus agents. Their experience illustrates the automation-to-agents shift: first, traditional automation tools were sturdy on the predefined path but brittle everywhere else. Heavy hard-coding, extensive configuration, and they could not interpret intent, make judgment calls, or reason about what mattered. Second, open-ended AI tools like ChatGPT Deep Search: intelligent but inconsistent. Same question, different results every time. For enterprise sales intelligence, where reliability matters, that unpredictability was unacceptable.

Lambda needed both intelligence and reliability. Their Head of Sales Intelligence, Joaquin Paz (not an engineer), built a research agent that monitors 12,000+ enterprise accounts, identifying buying signals and market opportunities. Result: $4B+ in pipeline identified, 24,000+ hours of research capacity added annually (equivalent to 12 full-time analysts), deployed in weeks.

What matters most: Lambda is an AI company. They have world-class AI engineers. They could have built this themselves. Their CTO concluded the opportunity cost of engineering time was too high. If a multi-billion dollar AI company chose to buy rather than build, it says something about the speed and reliability of the Nexus platform.

"We looked at traditional automation: it was reliable but felt heavy, lots of hard coding. With Nexus, we got both: intelligent and consistent."
"We've changed data sources, updated our account segmentation, adjusted priorities. The agent adapts. With the workflow tools we tried before, every change meant starting over."
Joaquin Paz, Head of Sales Intelligence, Lambda

A multi-billion euro telecom operator: tried automation tools first

A major European telecom (13,000+ employees, over half a billion euros in revenue) had existing automation infrastructure. Their bots executed the predictable steps reliably. But their highest-impact workflows (support, compliance, registration) involved ambiguous inputs, edge cases, and judgment calls that bots could not handle. Every exception required a human. They deployed a suite of Nexus agents across these functions. 40% of support capacity freed. Full regulatory compliance maintained across millions of interactions. 12-week deployment.

The difference: automation tools could handle the structured fraction of each workflow. Nexus agents handle the full 100%, including the edge cases, the ambiguous requests, and the compliance checks that require autonomous decision-making rather than predefined scripts.

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Key differences explained

Sturdy but brittle vs. intelligent and adaptive: a category difference

This is the fundamental distinction, and it matters more than any feature comparison.

UiPath automates by recording and replaying human actions. It watches how a person interacts with a screen and creates a script that repeats those actions. On the structured path, this works perfectly. But the moment something deviates (a field moves, a pop-up appears, a customer submits ambiguous data, an edge case requires a judgment call), the bot fails. It cannot interpret intent. It cannot hold a conversation to clarify what the user needs. It cannot decide what to do next. This is the core brittleness of RPA: it executes predefined scripts perfectly on the defined path, but it breaks on everything else. And at every break point, a human has to step in.

Nexus agents combine process execution with conversational intelligence and autonomous decision-making. They do not mimic screen actions. They reason about what needs to happen. An onboarding agent does not "click the submit button in the third column of the second row." It understands that customer data needs to be validated, checked against system requirements, and either approved or escalated based on specific business criteria. When the interface changes, when the data format varies, when an edge case appears, the agent adapts because it understands the purpose of the work, not just the sequence of clicks. When a customer's request is ambiguous, the agent can hold a conversation to clarify intent. When a situation requires judgment, the agent makes a decision or escalates with full context. These are the capabilities that replace the human judgment automation requires at every exception point.

UiPath recognizes this gap. Their CEO Daniel Dines has called agentic automation "act two" for UiPath, and the company is investing heavily in Agent Builder, Maestro orchestration, and the AI Trust Layer. These are meaningful steps. But adding AI capabilities on top of an RPA foundation is architecturally different from building agent-first. The underlying scripts are still rule-based. When the rule breaks, the AI layer built on top of it inherits that brittleness.

The maintenance problem: brittleness at scale

Most enterprises that deploy RPA discover a difficult truth: the maintenance burden grows faster than the automation value. This is the structural consequence of building on predefined scripts. Every time an application updates its UI, bots that interact with that application break. Every time a process changes, the bot needs to be rebuilt. Every time a new edge case appears, someone has to script a new rule. The bot executes perfectly until something shifts, and then it shatters.

The result is that most enterprises automate only the simplest, most stable processes and accept that the vast majority of automatable work stays manual. Not because they do not want to automate it, but because the cost of building and maintaining brittle scripts for complex, changing processes exceeds the benefit. The processes with the highest business impact stay manual because they involve too many exceptions, too much ambiguity, and too many judgment calls for scripts to handle.

Nexus agents do not have this problem because they operate at the business logic level, not the screen level. They connect to systems through 4,000+ API integrations, not through UI interactions. When an application updates its interface, the API stays the same and the agent keeps working. When a process changes, the agent's logic can be updated in hours, not rebuilt from scratch over weeks. And when a new exception appears, the agent reasons about it rather than stopping and waiting for a human.

Lambda's experience captures this directly: "We've changed data sources, updated our account segmentation, adjusted priorities. The agent adapts. With the workflow tools we tried before, every change meant starting over."

Platform vs. solution: the Forward Deployed Engineer difference

This is where Nexus diverges most sharply from UiPath and from most enterprise software companies.

UiPath sells a platform. You purchase licenses, then your team (or a systems integrator) builds, tests, and maintains bots. If you need help, professional services are available, but they are a separate engagement. The implicit assumption is that your organization has the resources and expertise to make the software work.

Nexus is a solution: the platform combined with embedded human expertise. Forward Deployed Engineers (FDEs) are real engineers who work alongside your team from day one. They help identify the highest-impact use cases, specifically the ones where RPA's brittleness creates the biggest gap between what is automated and what needs to be. They design agents for your specific workflows, including the exception paths and judgment calls that scripts cannot handle. They handle integration complexity so your team does not have to learn a new platform. They support change management because deploying AI at scale changes how work gets done, and people need to understand what is happening.

This is why Nexus has a 100% POC-to-contract conversion rate. Every pilot converts because Nexus does not sell software and disappear. The team stays engaged until the agents are delivering measurable value.

Business ownership vs. IT dependency

UiPath requires dedicated technical resources. Someone needs to map the process, record the bot's actions, script every exception path, test across environments, and maintain the bot when things break. And things break often, because the brittleness of predefined scripts means any change in the environment requires rework. Most organizations build an RPA Center of Excellence, a dedicated team of developers whose job is building and maintaining bots. This creates a bottleneck: business teams identify automation opportunities, then wait for the RPA team to build them. UiPath's Autopilot is designed to reduce this barrier, but complex automations still require technical expertise.

Nexus agents are built by business teams directly. Lambda's sales intelligence agent was built by their Head of Sales Intelligence, not an engineer. Orange's customer onboarding agents were built by their business team, not IT. The people who understand the workflow are the people who build and own the agent.

This is not just about speed (though agents deploy in days, not months). It is about ownership. When the business team owns the agent, they iterate on it continuously. They do not file a ticket and wait. They adjust, improve, and expand, because they understand both the business problem and the tool solving it.

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What about UiPath's AI and agentic capabilities?

UiPath is investing significantly in AI. It is worth understanding what they have built and how it compares.

Agent Builder lets users create AI agents within the UiPath platform. These agents can handle tasks that require reasoning and decision-making, extending beyond traditional rule-based bots. It is a meaningful evolution from pure RPA.

Maestro is UiPath's orchestration layer for managing workflows that combine traditional robots, AI agents, and human workers. It provides centralized oversight for scaling agentic automation across teams.

Autopilot is a conversational AI assistant that helps employees trigger automations, get answers from enterprise data, and complete tasks through natural language. It aims to make UiPath's capabilities accessible to non-technical users.

AI Trust Layer provides governance controls including PII redaction, audit logs, usage controls, and per-group permissions for AI operations.

These are real capabilities, and they represent UiPath's recognition that pure RPA is not enough for the complexity of enterprise work. But the architecture matters. UiPath is adding intelligence on top of a rule-based automation foundation. The underlying scripts are still predefined, still screen-level, still brittle when conditions change. Nexus was built with intelligence as the foundation from day one. The distinction shows up in practice:

• When something unexpected happens in UiPath's agentic system, the underlying bot architecture still constrains how the agent can respond. It still cannot hold a conversation with a customer or interpret ambiguous intent. In Nexus, agents reason about the exception, converse when clarification is needed, and make autonomous decisions without being constrained by screen-level scripts.
• UiPath's Agent Builder exists within the broader UiPath ecosystem, which means enterprises typically need existing UiPath infrastructure and expertise. Nexus agents deploy independently into whatever systems you already use.
• UiPath's AI features require technical resources to configure and maintain. Nexus agents are built by business teams with FDE support from day one.

UiPath's own acknowledgment is telling. They have described agentic automation as their "act two," an implicit recognition that act one (predefined scripts executing the structured path) has reached its limits for the kinds of complex, exception-heavy processes that drive the most business value. The question is whether adding intelligence on top of a brittle foundation produces the same result as building on intelligence from the start.

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Frequently asked questions

Can I use both UiPath and Nexus?

Yes, and in some cases it makes sense. UiPath can continue handling the structured path: simple, predictable, screen-based automations, especially for legacy systems without API access. Nexus handles everything that bots break on: workflows involving ambiguous inputs, exceptions, judgment calls, conversations, and multi-system coordination. They address fundamentally different types of problems and can coexist. You do not have to rip out what is already working.

We already invested heavily in UiPath. Is that wasted?

The investment is not wasted for the processes UiPath handles well: the stable, predictable, structured path. The question is whether those processes represent the full scope of what you need to automate. If the highest-value workflows are still manual because they involve ambiguous inputs, exceptions, conversations, and judgment calls that bots cannot handle, Nexus addresses that gap. You are adding the conversational intelligence and autonomous decision-making that replaces the human judgment automation requires at every exception point, not replacing what already works.

How long does it take to deploy Nexus alongside existing RPA?

Nexus does not depend on or conflict with existing RPA infrastructure. Most enterprise POCs go live within 2 to 6 weeks, with a Forward Deployed Engineer handling integration and configuration alongside your team. FDEs specifically focus on the workflows where RPA's brittleness creates the biggest gap, designing agents that handle the exceptions, judgment calls, and ambiguous inputs that bots cannot. Your team is not figuring out a new platform alone.

Is Nexus just "RPA with AI added"?

No, and this is the most important distinction. UiPath and other RPA vendors are adding AI features to their existing platforms, but the architecture is fundamentally different. RPA starts with predefined scripts that execute the structured path and tries to add intelligence on top. Nexus starts with intelligent agents that combine process execution with conversational intelligence and autonomous decision-making. Bolting AI onto a brittle, rule-based system does not change the underlying limitation. When the script breaks on an ambiguous input or an edge case, the AI layer does not help. Nexus agents are intelligent from the ground up: they interpret intent, hold conversations, make judgment calls, and reason about business logic rather than following scripted sequences.

What about UiPath's pricing vs. Nexus?

UiPath uses credit-based "Unified Pricing" or per-bot licensing, with enterprise deals often running into six figures annually. Costs scale with the number of bots, AI credits consumed, and platform usage. Nexus uses per-agent pricing tied to value delivered. Every engagement starts with a 3-month proof of concept with defined outcomes, so you see measurable results before committing to a larger investment.

UiPath has thousands of enterprise customers. Why should I trust a smaller company?

Scale of customer base and quality of outcomes are different things. Nexus is backed by Y Combinator and General Catalyst (the same investors behind Airbnb, Stripe, and Figma). Named enterprise customers include Orange Group (multi-billion euro telecom, 120,000+ employees) and Lambda (multi-billion dollar valuation, $500M+ ARR). The company is SOC 2 Type II, ISO 27001, ISO 42001, and GDPR certified. And the 100% POC-to-contract conversion rate means every enterprise that has tested Nexus has chosen to continue. That is a signal worth considering.

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Worth exploring?

If your RPA deployment handles the structured path but the highest-value workflows are still manual (because the ambiguous inputs are too varied, the exceptions require judgment, the maintenance is too costly, or the processes change too frequently for predefined scripts to keep up), it might be worth seeing how Orange, Lambda, and a major European telecom handled this. They all tried automation tools first. They came to Nexus when they needed agents that combine process execution with conversational intelligence and autonomous decision-making, replacing the human judgment that their automation required at every exception point. And they got Forward Deployed Engineers embedded with their teams from day one.

Every engagement starts with a 3-month proof of concept tied to specific outcomes. You can exit anytime.

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• Back to all comparisons -->