Agentic augmentation in private equity portfolio companies is a polarized activity in 2026, torn between:
1. Legacy companies vs. early adopters and more progressive R&D teams
2. Striving for operational efficiency vs. understanding risk and regulatory damage
3. Iterating on optimizing mundane tasks vs. protecting human time from editing AI slop or relying on fake, hallucinated data
The brute force is adding up, and we see conflicting results in different LMM companies we’ve been working in for years.
Rapid experimentation by churning tons of content or automated campaigns or images tends to work safely enough with an LLM layer in most cases. This was the first layer to automate – including customer support or knowledge base activities constrained to a limited data pool (or requiring a higher degree of creativity).
Anything tied to financial reports or regulations or compliance is far more deterministic, and where we’ve started with LLM attempts in early 2025, most of our agentic systems now incorporate 200 tools with 15+ APIs, scraping data in in-house databases, analysing through restful systems, APIs, and very deterministic DAGs, in a model that’s not too far off from automation systems we built for banking in 2012 or 2015 (pre-LLMs).
And one of the biggest differentiators today is training the AI muscle. Deploying agentic systems and harnesses continuously into different problems, a trial-and-error cycle that shapes the boundaries and the limits, one at a time.
This way, senior professionals get practical training on when agents do optimize and progress and when it’s an uphill battle that’s not there yet. Developing a new sense of tooling and systems knowledge into when to vibe code something, or what scraping abilities are effectives with LLMs, or when does data consolidation and augmentation work better through the LLM lenses vs. humans or outsourcing.
What level of accuracy is acceptable and needed, and when it makes more sense to get 3 red teams QA’ing agent output vs. relying on 200 humans pulling this data and making human mistakes.
Thousands and thousands of hours deep into agentic implementations, that muscle evolves just like any other craft of the past.
This is the biggest differentiation lever for mid-market and PE port cos we’ve been maximizing over the past 24 months to shape successful exits at high multiples, or post-merger integrations, or executing the first 100 days successfully. DevriX acts as an embedded value creation unit where needed, combining bleeding-edge strategy with fast-paced delivery one week at a time.
