Architecture · Blog Post
Agentic RPA with Robomotion: Letting AI Agents Run Your Bots Without Losing Control
Classic RPA breaks on anything unexpected: an invoice in a new layout, an email that asks two things at once, a portal that shows a pop-up nobody planned for. AI agents handle exactly that kind of mess, and they also make things up. Robomotion now puts both in one tool. The trick is to let the agent decide and let deterministic flows act. Here is how that works, and the architecture I use so clients get the flexibility without the surprises.
By Mehadi Hasan 8 min read
1. What "agentic RPA" means in Robomotion
Robomotion's own definition on its agentic RPA page is short: "RPA run by AI agents." In practice, the pieces are:
- Model packages for Claude, OpenAI, Google Gemini, OpenRouter, and Ollama for models running on your own hardware (AI agent docs).
- Agent nodes, including Google's Agent Development Kit for sequential, parallel, or looping agents, and the Hermes agent with memory, skills, and sub-agents.
- Tools. In the docs' words, "a tool is a node an agent is allowed to call itself." Your existing flows and nodes become the agent's hands.
- Skills: written instructions, not code, that tell the agent how your business does something.
- MCP, the open Model Context Protocol, for reaching capabilities outside Robomotion's own packages.
- Knowledge Base, for grounding answers in your own documents.
- Chat Assistant, a chat front end with a Guided mode and an agent-backed Conversational mode, so staff can talk to an automation instead of filling in a form (chat assistant).
2. The rule that keeps it safe: agents decide, flows act
The mistake I see most is giving an LLM a browser and a goal and hoping. It works in a demo and drifts in production. The pattern that survives real data is a split:
The agent handles judgement
Read the unstructured input, classify it, extract fields, decide which flow should run next, and write a human-readable summary of what it did.
Deterministic flows handle actions
Logging in, posting a bill, updating a CRM record, sending a payment file. Each is a tested flow with validation, exposed to the agent as a tool with a narrow, well-described contract.
The agent can only do what its tools allow, so the blast radius of a bad decision is limited to choosing the wrong tested action, which validation and approval steps can catch. It cannot invent an action you never built.
3. Guardrails I build into every agentic bot
- The fewest tools that do the job. An invoice agent needs "read invoice", "look up vendor", "post bill", and "send to review". It does not need email or file deletion.
- Validation after the model, not trust in it. Totals must add up, the vendor must exist, the date must be plausible. Fail validation, go to a person.
- Human approval for anything irreversible. Payments, deletions, and messages to customers wait for a click in the Chat Assistant or a review queue until the error rate earns more autonomy.
- Credentials the agent never sees. Robomotion says agents "act through the credential proxy and never hold your real secrets", and vault items are encrypted in the browser with AES-256 before upload (security model).
- Grounded answers. For questions about policy or products, the agent answers from the Knowledge Base and says "I don't know" when the documents do not cover it.
- Local models for sensitive data. With the Ollama package, documents can be processed by a model on your own server, so the content never goes to a third-party AI provider.
- A log a manager can read. Every run records input, decision, tool calls, and outcome. Robomotion's audit log records consequential actions with the actor and before/after state.
4. Three agentic bots worth building first
Invoice intake with exceptions handled
A classic bot downloads invoices from supplier portals. The agent extracts fields from any layout, matches the vendor, and either calls the "post bill" flow or routes the invoice to a person with a one-line reason. New suppliers stop breaking the bot. See how I compare classic OCR and vision LLMs for invoices.
Shared-inbox triage
The agent reads each email, classifies it (order status, change request, complaint, spam), pulls the facts from your systems through flows, drafts a reply, and queues it for approval. Routine requests that pass checks can later go out automatically.
An internal "ask the bot" assistant
Staff ask in chat, "pull last month's shipments for client X" or "has supplier Y been paid?". The agent picks the right flow, runs it on a robot, and returns the file or the answer. Your existing bots become self-service.
5. Cost and plan notes
Robomotion plans include monthly AI credits (2,500 on Solo, 7,500 on Pro, 25,000 on Team, per the pricing page). If you connect your own model provider, its usage bills separately. Two habits keep this cheap: use the model only for the judgement steps, never for clicking through pages a flow can handle, and pick the smallest model that passes your accuracy tests. More on plans in Robomotion pricing explained.
Have a process that keeps breaking your bots?
Messy documents, free-text emails, and exceptions are where agents earn their cost. Describe the process and I will tell you which part an agent should own, which part should stay a deterministic flow, and what it would take to run safely. See my AI agent development and RPA development services.
Get a free 30-min automation auditFrequently asked questions
What is agentic RPA?
RPA where an AI agent makes the judgement calls, such as reading an unstructured document, classifying a request, or choosing the next step, and deterministic bot flows carry out the actions. Robomotion describes it as RPA run by AI agents.
Which AI models does Robomotion support?
Robomotion's documentation lists packages for Claude, OpenAI, Google Gemini, OpenRouter, and Ollama for local models, as of October 2026.
Is it safe to let an AI agent run business processes?
It can be, with the right design: give the agent only the tools it needs, validate its output, require human approval for irreversible actions, keep credentials out of its reach, and log every decision. Start with approval on everything and widen autonomy as the error rate proves itself.
Can sensitive documents stay off third-party AI services?
Yes. Robomotion supports Ollama, so documents can be processed by a model running on your own server instead of a hosted AI provider.
Features and quotes checked October 30, 2026 against Robomotion's agentic RPA page, chat assistant page, and documentation linked above. The platform is moving quickly; check the current docs before building.