AI agents explained: From automation to autonomy

4 Mar 2025|Learning

AI agents are everywhere, but what do they mean? The term is used for everything from basic automation to fully autonomous decision-making, making it one of AI’s most overhyped and misunderstood concepts. While companies claim to use AI agents, most are stuck in rigid workflows rather than real autonomy. So how do we define AI agents correctly, and where do businesses stand today? Let’s cut through the noise and get to the truth.

Two persons pointing at monitors and seen in discussion

The AI agent maturity curve

At Futurice, we see AI agents evolving along a clear maturity curve. They start as fixed, rule-based assistants and gradually move toward orchestrated autonomy, where they can make independent decisions.

Here’s what that journey looks like:

1. Fixed workflow + LLM enhancement

This is where most companies are today—AI as an automation assistant rather than a decision-maker. The agent helps process messy data, classify information, and extract insights, but it follows a strict workflow. Example: An AI-powered email sorting system that categorizes messages but doesn’t decide how to respond. The limitation: It’s still just automation. The AI doesn’t think—it executes.

2. Fixed workflow + LLM judgment calls

A small step forward. Here, AI starts making context-aware decisions, but still within a structured workflow. It can compare information, detect inconsistencies, and recommend actions. Example: A financial AI that scans expense reports for fraud, flagging anomalies for further review. The limitation: AI is making calls, but only within predefined boundaries. It’s not flexible, and it can’t adapt on the fly.

3. Autonomous agents with tools and guardrails

This is where things get interesting. AI agents no longer just execute tasks—they choose the right tools and workflows dynamically to achieve a goal. Example: An AI-powered DevOps assistant that detects infrastructure failures and autonomously picks the best fix from a set of options. The limitation: Companies hesitate to give AI this much control. Guardrails are necessary, but overly strict rules can cancel out AI’s potential.

4. Total freedom and tools

The final stage—AI agents that can access raw data and systems, make complex decisions, and operate with full autonomy. Example: AI-driven supply chain optimization, where an agent reacts to global disruptions and adjusts logistics without waiting for human approval. The challenge: Trust. Companies fear losing oversight, but without gradually increasing AI autonomy, they risk falling behind.

Why most companies are stuck in stages 1 and 2

Despite the AI hype, most businesses are still in basic automation territory. Why?

Lack of trust & understanding: Handing over key decisions to AI still feels risky as the reasoning behind the outputs and explainability of models are unclear.
Legacy systems: Many companies rely on tech stacks not designed for AI-driven workflows.
Short-term thinking: The focus is on quick automation wins rather than long-term AI evolution. Being stuck in these early phases means missing the real potential of AI.

What’s next: moving beyond AI as an assistant

Moving from fixed workflows towards autonomous agents, organizations need to:

Rethink AI governance – Instead of restricting AI, design oversight that enables autonomy.
Prioritize experimentation – AI agents require iteration. Start small, scale smart, and keep learning.
Embrace AI-augmented decision-making – AI isn’t replacing humans, it frees us from repetitive tasks and enables us to focus on more value adding, strategic work.

The future of AI agents isn’t about automating more tasks. It’s about rethinking what adds value and how work is done. AI should be a decision-making partner, not just an assistant.

So, where is your company on this AI maturity curve? And more importantly, what’s stopping you from moving forward?