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Comment · 11 months ago
Very curious to hear how you're solving AI memory related issues! I'm currently exploring context portability for more personalised AI agents.
Comment · 1 year ago
That's precisely what we found in our research as well! We outlined it in our observations too (excerpt below):

The most successful deployment strategies we’ve seen started with:

simple and specific use cases with clear value drivers, that were low risk yet medium impact;

weren’t majorly disruptive to existing workflows;

preferably automating a task that the human user dislikes (or was outsourced);

the output of the workflow can be easily/quickly verified by the human for accuracy or suitability; and

demonstrated clear ROI quickly

Given the current levels of technological development, AI Agents work best when narrowly applied to very specific tasks and operating under a specific context. For instance, we’ve seen this in healthcare with revenue cycle management processes (claim and denial management) that health systems were already outsourcing to third-party providers.

The land-and-expand strategy for AI agents is very different to traditional SaaS. Given enterprises are increasingly under pressure from the C-Suite to incorporate AI into their work, there are plenty of opportunities for startups to “land” but it’s much harder to “expand” – and not only that, it’s taking much longer to expand even when they want to expand, because it’s a use case by use case rollout.

Much like the iconic Volkswagen ad, sometimes it’s better to “Think Small” and build trust first, rather than attempt too many use cases (and excessively complex use cases) right off the bat.

Comment · 1 year ago
More than the "actual" problem, the "perception" of the problem is worse. Workflow integration is more to do with users having to rethink their workflows, their roles, and how they work with AI. As for data privacy concerns, even where startups have taken measures to overcome the problems, very often enterprises still remain concerned (making this more of a perception problem than an actual problem). That's why I focused on the non-technical aspect of it!
Comment · 1 year ago
We recently spoke with 30+ startup founders and 40+ enterprise practitioners who are building and deploying agentic AI systems across industries like financial services, healthcare, cybersecurity, and developer tooling.

A few patterns emerged that might be relevant to anyone working on applied AI or automation:

- The main blockers aren’t technical. Most founders pointed to workflow integration, employee trust, and data privacy as the toughest challenges — not model performance.

- Incremental deployment beats ambition. Successful teams focus on narrow, verifiable use cases that deliver measurable ROI and build user trust before scaling autonomy.

- Enterprise adoption is uneven. Many companies have “some agents” in production, but most use them with strong human oversight. The fully autonomous cases remain rare.

- Pricing is unresolved. Hybrid models dominate; pure outcome-based pricing is uncommon due to attribution and monitoring challenges.

Infrastructure is mostly homegrown. Over half of surveyed startups build their own agentic stacks, citing limited flexibility in existing frameworks.

The article also includes detailed case studies, commentary on autonomy vs. accuracy trade-offs, and what’s next for ambient and proactive agents.

If you’re building in this space, the full report is free here: https://mmc.vc/research/state-of-agentic-ai-founders-edition...

Would be interested to hear how others on HN are thinking about real-world deployment challenges — especially around trust, evaluation, and scaling agentic systems.

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