Agentic workflows are no longer just features or add-ons to existing products. They’re becoming real product infrastructure for a new wave of companies. This changes how companies build, how teams operate, and how investors evaluate technical maturity. It also signals a new type of company emerging in the ecosystem and a wave of changes to how we assess products, companies and investment opportunities.
Workflow automation is becoming the new norm
More teams are moving from simple LLM features to multi-step workflows that execute reliably on their own, embedded directly into their products. The value proposition is shifting from “AI-enhanced tasks” to “multi-step workflows that execute reliably on their own”.
We’ve long seen AI-assisted research tools that help teams draft user interview guides. Now, we’re seeing companies offer an agent that designs the study, recruits participants, runs adaptive interviews and delivers a full insight report you can trust. Similarly, many teams are used to AI-generated meeting summaries, but now these AI-native companies are offering services built on agentic workflows. Instead of simply summarising a meeting, the agent takes standup notes, turns them into tasks, schedules follow-ups, assigns owners and updates the weekly plan from start to finish.
Rising technical maturity is enabling the next wave
Large language models have improved to a point where they can take context, make decisions and move through a sequence of steps with far fewer errors than just a year ago. At the same time, the tooling around orchestration – like retries, fallbacks, monitoring, evaluation and workflow control – has become more stable and easier for teams to use. These shifts open a new competitive playing field for companies that want to use AI not just as an enhancement, but as the core engine for how work gets done.
The lower cost of production pushes companies to automate deeper layers of work, not just surface interactions or isolated tasks. Instead of using AI to improve a single step, teams are starting to automate the whole process behind it. It allows companies to automate the “hidden work”: repetitive tasks, checks and small decisions that traditionally slow teams down.
There’s a lot of fluff, a lot of non-work happening in teams. We’re building an AI teammate that understands the company, the process, the routines and where the team is heading – and helps eliminate that non-work.
Steffen D. Sommer, CTO at Palette
This new wave of AI-native companies isn’t adding small AI features on top of existing workflows. They’re building products where the agentic workflow is the product. Instead of improving one step in a process, the core value comes from orchestrating the whole workflow from start to finish.
When the core value of a product is delivered by an agentic loop, everything changes – the architecture, the roadmap, and even the definition of stability.
Rasmus Møller-Nielsen, Managing Partner at Lab08
Some companies offer dynamic interview agents that adapt questions based on context. Others automate team coordination by converting standup updates into tasks and follow-ups. Some tools use multimodal evaluation to analyse images or video in real time. Others build domain-specific agents capable of taking a problem, interpreting context and delivering a full outcome without manual intervention.
New risk categories and why they matter
So what does this new wave of AI-native companies mean for technical risk? When the cost of production drops and technical capabilities rise, the risk profile changes too.
Agentic workflows introduce behaviours that didn’t exist in traditional software, and this means we need to assess these products in new ways. Reliability becomes more important because – in short – agents chain actions together.
Rasmus Møller-Nielsen, Managing Partner at Lab08
Uncertainty handling and guardrails must be built into the system. Evaluation pipelines and monitoring need to be continuous, not occasional. Versioning and observability become essential, because small changes in prompts or models can impact the entire customer workflow.
Agentic systems amplify both the strengths and weaknesses of the underlying architecture.
Traditional due diligence asks whether a company uses AI or not. Modern due diligence must now start to ask questions about how agents behave inside the system. Technical maturity now shows up in orchestration quality, uncertainty handling, data flow discipline, and the company’s ability to monitor and control multi-step behaviour.
These signals – and others, help investors understand whether a product can scale safely.
The new generation of AI-native companies are building deeper technical infrastructure, not just AI features. They change the expectations we have for product quality, internal tooling and due diligence. They pull founders, engineers and investors into the same conversation: how to build products that act reliably, not just answer.
For startups, this creates new opportunities to build differentiated products with real leverage. For investors, it introduces new signals of quality, stability and risk. And for the wider ecosystem, it marks a shift in how tomorrow’s software companies will be built – and how we should assess this type of business.
