# From Reactive to Proactive: How AI Agents Are Redefining Work

> Author: Chris Jon Graf (AI Strategist & CEO)
> Updated: 2026-08-14
> URL: https://ai-outsourcing.ch/insights/from-reactive-to-proactive-how-ai-agents-are-redefining-work

## Summary

Proactive AI agents differ fundamentally from reactive assistants like ChatGPT: they receive a goal, plan the steps themselves, use tools, check outcomes and escalate to humans only when needed. For Swiss mid-market companies, this means deploying agents and supervising people, not simply hiring more staff.

## From Answer Machine to Goal Achiever

A reactive AI assistant such as ChatGPT, Siri or Alexa waits for your question, delivers an answer — and the next step is yours to decide. A proactive AI agent reverses this pattern: you set a goal, the agent plans the necessary steps itself, selects the right tools, executes them, checks the outcome and adjusts course — until the goal is reached or a situation arises that it deliberately escalates to you. This shift is what is changing how office and knowledge work gets organised inside Swiss companies.

## What Actually Separates an Agent from a Chatbot

A capable language model with fast inference is enough for a reactive assistant. A proactive agent needs considerably more: a persistent state that survives beyond a single session, mechanisms that activate it without a user prompting it (so-called wakeup triggers), clear guardrails for its actions, and a binding rule for when a human must be brought in. The Zylos maturity model describes this progression across five levels — from purely reactive systems to agents that identify opportunities on their own and act without being asked.

1. Level 0 – Reactive: the agent only responds to explicit requests.
2. Level 1 – Assisted: the agent suggests next steps but does not act on its own.
3. Level 2 – Semi-autonomous: the agent executes defined tasks with human approval.
4. Level 3 – Autonomous with oversight: the agent plans and acts independently, escalating only exceptions.
5. Level 4 – Self-initiated: the agent identifies opportunities on its own and acts without being prompted.

> The difference between reactive and proactive AI is not a question of the model, but of the architecture: persistent state, autonomous triggers and clear escalation rules are what turn an assistant into an agent.
>
> — Zylos maturity model

## The Numbers Behind the Shift

This shift is already well underway, even if it rarely reaches full maturity yet. The Anthropic State of AI Agents Report 2026 shows how quickly organisations are moving from isolated tasks to connected workflows — and where the biggest gaps remain.

**57%** — of organisations already use AI agents for multi-stage workflows, 16% of them cross-functionally (Anthropic, 2026)

**81%** — plan more complex agent use cases for 2026 (Anthropic, 2026)

**60%** — highest measured impact area: data analysis and reporting, followed by internal process automation at 48% (Anthropic, 2026)

**15%** — of organisations run orchestrated multi-agent systems; 42% are at first deployments, 43% are scaling (Deloitte)

This gap between first pilots and genuine orchestration is no accident. BCG describes agentic AI as the most transformative form of applied AI — but one that demands a structural reinvention of entire end-to-end processes, not just a new tool bolted onto an existing workflow. Organisations that skip this step tend to get stuck at level 2 or 3, while the real value only emerges with orchestrated, goal-driven processes.

## Where Agents Are Already Doing the Work

At Rivian, AI agents running on Amazon Bedrock AgentCore handle ERP-related tasks and connect directly to SAP S/4HANA via OData and the Model Context Protocol. The result: more than 15 days of manual work eliminated per cycle — time that previously went into data reconciliation and re-entry.

In Lemvigh-Müller's procure-to-pay process, SAP agents read incoming emails and PDF invoices, extract the relevant data and automatically route cases to the right team — work that case handlers previously did line by line.

Barclays uses agentic orchestration built with Camunda for client due diligence: an orchestration layer acts as a control plane for the entire review process, governing which agent handles which sub-step and when — traceable and auditable, a critical requirement in regulated industries.

At RingCentral, agents plan, decide and act across multi-step tasks — in the contact centre they reduce average handling time and resolve tickets proactively instead of merely reacting. Gartner expects agentic AI to autonomously resolve around 80% of all customer service issues by 2029. The same logic pays off in risk management: Stripe's agentic systems detect 95% of card-testing attacks in real time while reducing unnecessary customer friction by 20%.

## Why This Is More Than a New Tool in an Old Process

Springer BISE describes accurately what is happening in 2026: business process management is shifting from rigidly predefined control flows toward goal-driven orchestration. An agent bolted onto an existing process designed for humans stays well below its potential. The real leverage appears when the process itself is redesigned with the agent as the starting point, not an add-on.

> **Deploy Agents, Supervise People — Instead of Hiring**
>
> For many Swiss mid-market companies, this shift raises a new baseline question: instead of automatically opening another headcount when workload grows, leadership examines whether an agent can take on the task — while people retain oversight, exception handling and strategic direction.

## The Swiss Frame: Governance, revDSG and Trust

The moment an agent makes an autonomous decision that affects a person — approving a payment, say, or prioritising a case — Switzerland's revised Data Protection Act (revDSG) and its provisions on automated individual decisions come into play. Organisations introducing agentic workflows need to clarify from day one where transparency obligations apply and where a human retains the final call. There is also the question of data infrastructure: agents accessing sensitive data across multiple systems need a well-designed access and logging logic — for especially sensitive data, sovereign infrastructure such as confidential computing, for instance built on Apertus, can be an additional building block.

## From Tooling Decision to Leadership Decision

Agentic AI cannot simply be handed to the IT department and checked off a list. Because it changes processes, accountability and, in some cases, job profiles, its introduction belongs on the executive agenda. A Swiss podcast episode explores how mid-sized companies are actually anchoring AI as a leadership topic rather than an IT project: [AI in the mid-market as a management topic, not an IT project](https://www.ki-podcast.ch/ki-im-mittelstand-management-thema-nicht-it-projekt).

> **Start Small, With Oversight**
>
> The most pragmatic entry point is rarely a leap to level 4. A single, clearly scoped process with a defined human-in-the-loop policy delivers real experience within weeks — and the foundation for gradually allowing more autonomy.

## The Next Step for Swiss Decision-Makers

The question is no longer whether proactive agents will change office and knowledge work, but how quickly and how deliberately your organisation shapes that change. Which process is the right starting point, what escalation rules it needs and what governance building blocks make it viable depends on your specific starting position — and that is exactly the point where a conversation is worth more than experimenting alone.

## FAQ

### What distinguishes an AI agent from a chatbot like ChatGPT?

A chatbot responds reactively to individual questions; an AI agent pursues a given goal independently across multiple steps, uses tools and systems to do so, checks intermediate results and escalates to a human only in exceptional cases.

### Do AI agents eliminate jobs?

AI agents mainly take over repetitive, multi-step tasks such as data entry or case routing. The role of employees shifts more toward oversight, exception handling and strategic direction rather than disappearing entirely.

### What role does Switzerland's revDSG play for autonomous AI agents?

When an agent makes an automated decision affecting a person, the revDSG's provisions on automated individual decisions apply. Companies then need to meet transparency obligations and define where a human retains the final decision.

### Where do AI agents deliver the greatest benefit today?

According to the Anthropic State of AI Agents Report 2026, the largest effects appear in data analysis and reporting (60%) and internal process automation (48%). Concrete examples also exist in procurement, compliance and customer service.

### Does every company need a fully autonomous multi-agent system right away?

No. According to Deloitte, only 15% of organisations currently run orchestrated multi-agent systems. A single, clearly scoped process with defined oversight is the more realistic and lower-risk starting point.

### What does an agent need technically that a reactive assistant does not?

A proactive agent needs persistent state across sessions, mechanisms to activate itself without user input, clear guardrails for its actions, and a binding rule for when a human must be involved.

## Sources

- [The 2026 State of AI Agents Report](https://resources.anthropic.com/hubfs/The%202026%20State%20of%20AI%20Agents%20Report.pdf)
- [Agentic Workflows: 2026 Enterprise Guide](https://www.ringcentral.com/us/en/blog/agentic-workflows/)
- [BCG Executive Perspectives Agentic Enterprise Operations](https://www.bcg.com/assets/2026/executive-perspectives-applied-ai-with-agentic-enterprise-operations.pdf)
- [How Rivian Accelerated Finance Operations with AI Agents on Amazon Bedrock](https://aws.amazon.com/blogs/awsforsap/how-rivian-accelerated-finance-operations-with-ai-agents-on-amazon-bedrock/)
- [Agentic AI – Springer BISE](https://link.springer.com/article/10.1007/s12599-026-01009-w)
- [The path to agentic transformation – Deloitte Insights](https://www.deloitte.com/us/en/insights/industry/technology/path-to-agentic-transformation.html)
- [How Barclays is re-engineering client due diligence with agentic orchestration](https://camunda.com/blog/2026/07/barclays-agentic-client-due-diligence/)
- [Production-grade AI agents for financial compliance: Lessons from Stripe](https://aws.amazon.com/blogs/machine-learning/production-grade-ai-agents-for-financial-compliance-lessons-from-stripe/)
- [What are Agentic Workflows? – Databricks](https://www.databricks.com/blog/agentic-workflows)
- [SAP: Lemvigh-Müller – Casting a new wholesale standard with agentic AI](https://www.sap.com/asset/dynamic/2026/06/be80dd33-587f-0010-bca6-c68f7e60039b.html)
- [Proactive AI Agents: From Reactive Assistants to Autonomous Monitors – Zylos Research](https://zylos.ai/research/2026-05-28-proactive-ai-agents-autonomous-monitors/)