Automation & agents.
Automate repetitive tasks to free up time for what matters — without losing control. We pick the right tool (RPA, orchestration or an AI agent), keep a human at the sensitive points, and set the guardrails that make automation reliable rather than risky.
What are agentic automation and RPA?
RPA (robotic process automation) automates repetitive tasks by imitating human actions on existing interfaces (clicks, keystrokes, copy-paste); it is useful for linking systems that lack an API, but stays fragile to screen changes. Workflow orchestration chains deterministic steps via APIs and events: more robust, testable and observable. Agentic automation relies on a large language model (LLM) that, from a goal expressed in natural language, plans a sequence of actions and calls tools to carry it out — suited to variable or unstructured tasks. These three approaches are often combined. The key success factor is not the technology but the framing: automate an already-sound process, keep a human in the loop at sensitive points, and set guardrails (limits, logging, supervision).
AI agent (agentic automation) — A software system that, from a goal expressed in natural language, plans and executes a sequence of actions by calling tools (search, APIs, code), adapting to intermediate results. Unlike a fixed script, an agent decides its own steps — which is precisely why it needs guardrails and supervision.
- RPA / orchestration / agent
- The right tool for the case
- Human-in-the-loop
- Human validation at sensitive points
- Guardrails
- Limits, logging, recovery
- Observability
- Every action traced
Automate when it is measurably useful
Automating a bad process only speeds up the mess. Before automating, we check that the task is stable, repetitive and clearly valuable — otherwise it is better to simplify it first. The goal is not to automate for its own sake, but to free up human time for what needs judgement, handing the machine what is genuinely mechanical.
RPA, orchestration, agents: three distinct tools
- RPA — imitates a human's clicks and keystrokes on existing interfaces. Useful for systems without an API, but sensitive to the slightest screen change.
- Workflow orchestration — chains deterministic steps via APIs and events. Robust, testable and reproducible: the default choice when interfaces allow it.
- AI agents — LLM-driven, they plan and call tools for variable or unstructured tasks. Powerful, but non-deterministic: they demand guardrails.
Human in the loop
Not every action should be fully automatic. On sensitive or irreversible decisions — a payment, a message to a client, a deletion — we insert a human validation point (human-in-the-loop). Automation prepares, proposes and runs the mechanical part; the human keeps control where judgement and accountability matter.
Guardrails and observability
- Explicit limits — scope of action, ceilings and least-privilege permissions.
- Logging of every action for audit and diagnosis.
- Error handling and recovery — idempotence, retries, rollback.
- Continuous supervision — measure failures and drift rather than assume they are zero.
How we work
- 01
Map
Inventory processes, measure their frequency and value, spot what is worth automating.
- 02
Choose the tool
Arbitrate between RPA, orchestration and an AI agent based on the task and interfaces.
- 03
Design
Build the automation with guardrails, validation points and error handling.
- 04
Supervise
Roll out in stages and track executions to adjust continuously.
What you get
- A map of processes and the tasks worth automating
- A feasibility analysis: RPA vs orchestration vs agent
- Design of workflows or agents with guardrails
- Human validation points (human-in-the-loop)
- Logging and observability of executions
- Error handling and recovery (idempotence, rollback)
- Progressive rollout and supervision
- Documentation and skills transfer
Frequently asked
Answers to the most common questions — timelines, sectors, compliance, hosting, methodology.
What is the difference between RPA and an AI agent?
RPA follows a fixed script: it reproduces exactly the same clicks and keystrokes, without adapting. An AI agent, driven by a language model, plans its steps from a goal and adjusts to intermediate results. RPA is predictable but rigid; an agent is flexible but non-deterministic and needs guardrails. We combine them depending on the nature of the task.
Should everything be automated?
No. Automating an unstable or ill-defined process only amplifies its flaws. We automate what is repetitive, stable and clearly valuable, and leave to humans what needs judgement. Sometimes the best decision is to simplify the process before even thinking about automating it.
Can an AI agent act on its own, without supervision?
Not on sensitive actions. An agent is non-deterministic: it can fail in unexpected ways. We place human validation points on irreversible decisions, restrict its scope of action and log everything. Autonomy is earned gradually, as its reliability is measured.
How do you stop an automation from getting out of control?
Through guardrails and observability. We limit scope and permissions, log every action, make operations reversible, and monitor failure rates continuously. An automation with no measure of its own behaviour is a risk, not a gain.
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