The change you don't see coming until it's already happened
In 2022, talking about AI agents in business was science fiction. In 2023, it was a lab experiment. In 2024, it was a promise. In 2025, it's infrastructure.
The difference is not in a single spectacular technological breakthrough. It lies in the silent convergence of three factors that together create a window of opportunity that did not exist before.
Factor 1: the cost of tokens has fallen 95%
In January 2023, processing one million tokens with GPT-4 cost approximately $60. In June 2025, equivalent or superior models cost less than $3 for the same volume. A 95% reduction in two and a half years.
This is not a technical detail. It is what transforms an "interesting but expensive" use case into one with positive ROI in weeks. An agent managing 500 emails a day used to cost more to operate than the salary of the person it replaced. Now it costs pennies.
"A 95% reduction in token costs over two years turns expensive experiments into profitable infrastructure."
Factor 2: APIs are stable and models are reliable
The early language models had a fundamental problem for business use: they were unpredictable. The same instruction could produce completely different results in two consecutive calls. For an entertainment chatbot, that is acceptable. For an agent managing orders or contracts, it is unacceptable.
The 2025 models have far superior instruction-following capabilities. Function calling is robust. Structured JSON is reliable. Complex system instructions are respected. It is not perfect, but it is good enough for production.
Factor 3: the tooling ecosystem has matured
Building an AI agent in 2023 meant writing everything from scratch: orchestration, memory, context management, error handling, retries. It was a 6-month engineering project for something basic.
In 2025 there is a mature ecosystem of frameworks, connectors and integrations that allows building robust agents in weeks. Integrations with ERP, CRM and standard business tools are available and documented.
What does this mean for a traditional business?
It means the window for adopting this technology with competitive advantage is closing. Companies that deploy AI agents in 2025 will operate with a cost structure that competitors waiting until 2027 will not easily match.
- Early adopters build their own training data that improves their agents over time
- They develop internal knowledge about what works in their specific sector
- They create processes and organisational culture adapted to operating with AI, which is harder to copy than the technology itself
- They attract higher-value human talent by freeing their teams from repetitive tasks
The question is no longer whether, but when and how
Companies that in 2025 are still in the "evaluating whether AI is for us" phase are losing time they will not recover. The right question is not whether to adopt AI agents, but how to do so in a structured way, minimising risk and maximising return from the first month.
That is exactly what we do at labrobotics: we do not sell technology, we design the complete agent system adapted to your company's real structure, deploy it gradually and measure the impact from the first week.