First act
01RPA
Software robots handle rule-based, repetitive work. Structured data, a fixed flow.
AGENTS THINK.
ROBOTS ACT.
PEOPLE STEER.
The second act of automation has begun. RPA handled repetitive work, AI made it smarter. Agentic automation builds an autonomous layer of work that understands the goal, forms a plan and orchestrates robots and people.
Agentmake a plan…
Systems
Robots
Human
The Evolution of Automation
FROM RPA TO AGENTIC AUTOMATION
Automation has evolved from work that is done to work that is thought through. Each act was built on the one before it.
Interlude
02AI-supported automation
Semi-structured data is processed with vision, language and prediction models; robots grow smarter.
Second act
03Agentic automation
An autonomous layer of work that understands the goal, forms a plan and orchestrates robots, systems and people.
How It Works
ONE FLOW, THREE DIFFERENT STRENGTHS
The essence of agentic automation is bringing the intelligence of agents, the power of robots and the judgement of people into the same orchestration.
Agents think
They interpret the goal, weigh the context, build a step-by-step plan and pick the right tool.
Robots act
They execute the plan safely through RPA bots, APIs and applications, with speed and accuracy.
People steer
They approve critical decisions, resolve exceptions and set the strategy. Autonomy always stays under control.
The Agent Loop · How It Works
SENSE. REASON. ACT. LEARN.
The agent repeats this loop until it reaches the goal, and each round decides better than the last. The whole loop runs inside an auditable orchestration layer.
01Sense
02Reason
03Act
04Learn
Sense
Reads documents, data, APIs and system events; interprets the context.
Reason & plan
Weighs the options and determines the steps and the order of tools that lead to the goal.
Act
Does the work through a robot, an API or an application; updates records and starts flows.
Learn
Evaluates the outcome and improves the next decision with memory and feedback.
Orchestration
AUTONOMY DOES NOT REACH PRODUCTION WITHOUT A CONTROL PLANE
An agent being intelligent is not enough. The real value appears in the orchestration layer that coordinates agents with robots, APIs, documents and people: the place that governs how work flows, who approves what, and the trace of every action.
Roles and permissions
What each agent can access, which tool it will use and its decision boundary are defined.
Sequencing and handover rules
How work flows and is handed over between agents, robots and people is managed.
Auditable records
Which action was taken on the basis of which data is logged end to end and stays traceable.
Human approval points
Critical steps, long-running processes and exceptions are raised for human approval.
Native × UiPath
PRODUCTION, NOT A DEMO
We do not leave the idea at demo stage: we pick the scenario, design the architecture, build the integration, define the governance and take the solution into production.
01
Use case selection
We identify the right starting area based on business impact, feasibility and risk level.
02
Agent architecture design
We design the agent's goal, data sources, tool set and decision boundaries.
03
Integration and orchestration
We bring the API, RPA, document processing and data layers together in a Maestro-style control plane.
04
Governance and security
We define role-based access, human approval, logging and control points.
05
Pilot and measurement
We take the first working scenario live and measure its performance and business impact.
06
Go-live and scaling
We roll the successful scenario out more widely and scale with new agents and processes.
Use Cases
WHERE DOES AGENTIC AUTOMATION CREATE VALUE
The highest value appears in processes where data, documents, decisions and actions flow across more than one system.
Finance & Accounting
Invoice reconciliation agent
Compares invoice, order and delivery; flags discrepancies and starts the approval flow.
Sales & CRM
Sales support agent
Combines customer history, product information and quoting rules to propose the next best step.
Risk & Compliance
Compliance and control agent
Checks policies, documents and transaction records; raises risky cases for human approval.
Operations & Service
Field service optimisation agent
Weighs the request, team availability and parts information to propose the best intervention plan.
Customer Service
Request triage agent
Sorts incoming requests by content, priority and category and routes them to the right team.
Supply & Logistics
Sourcing and procurement agent
Evaluates supplier, quote, contract and approval data to speed up purchasing.
Controlled Autonomy
AUTONOMY ONLY SCALES WITH TRUST AND GOVERNANCE
Enterprise agents must be not only intelligent but controllable. Decision boundaries, data access, human approval and traceability are designed in from the start; this trust layer sits at the centre of our projects.
Human-in-the-loop approval and intervention points
Role-based access and clear decision boundaries
End-to-end auditability, logging and traceability
Security, data protection and regulatory compliance
Continuous monitoring, performance measurement and a feedback loop
FREQUENTLY ASKED QUESTIONS
RPA executes rule-based, repetitive work along a fixed flow with structured data. Agentic automation adds a layer that understands a goal, builds a plan and orchestrates robots, systems and people. They are not alternatives: the agent is the interpretation and decision layer, the robot the execution layer, and both run in the same flow.
A chat assistant produces an answer; an agent takes action to reach a goal. It reads documents, data, APIs and system events, weighs the options and decides the order of steps, does the work through a robot or an API, updates records, and improves its next decision by evaluating the result. The difference is not in the text it produces but in the trace it leaves across the systems.
Autonomy should not reach production without a control plane. What each agent can access, which tool it may use and where its decision boundary lies are defined; critical steps, long-running processes and exceptions are routed to human approval. That boundary is widened gradually as the process matures and the audit record builds up, not in one step.
Yes; this is one of the core functions of the orchestration layer. Which action was taken on the basis of which data is logged end to end and stays traceable, and rules govern how work flows and where it is handed over between agents, robots and people. Auditability is part of the design, not a layer added afterwards.
It is not required, but an existing RPA investment accelerates the work: the robots and integrations that will carry out the agent's plan are already in place. Organisations without RPA can still start; in that case the execution steps are built on APIs and application integrations. What matters is not whether robots exist, but how accessible the process and its data are.
When scenario selection, architecture, integration, governance and go-live are planned as one flow. What stalled pilots have in common is that integration and governance were left for later; when both are defined from the outset, the pilot becomes a production candidate directly.
Our other services
RPA Consulting
Analysing repetitive manual processes and realising automation opportunities with RPA.
ExploreTest Automation
Automating software testing to improve quality, speed and sustainability.
ExploreProcess Mining
Analysing how business processes actually run to surface bottlenecks, deviations and improvement areas.
Explore
Agentic Automation Call
WHICH PROCESS BENEFITS MOST FROM AGENTIC AUTOMATION
In a short 30-minute assessment call, let's identify the most suitable starting scenarios in your organisation together.