Alex

I think you are reaching a similar conclusion to us over @cloudflare. The engineer's role is shifting more towards product. We have been referring to this as "outcomes engineering". o16g.com

Harness engineering is as important as model capability scaling. AI Agents are 50% a harness story. "Natural-Language Agent Harnesses" proposes moving harness logic out of code and into the native language of LLMs: Natural Language. Turns the agent harness into a portable artifact that you can experiment with, improve and execute in a shared runtime. Agent harnesses will mature into a first class citizen of the AI Agent ecosystem. We will have people who are Agent Harness engineers and experts.

Databricks held a webinar on agentic AI with 3 large enterprise CIOs / AI specialists. My punchline takeaway is that agentic AI is very early and very difficult to implement. This was an opportunity for these customers to boast about their AI progress and no one could cite a tangible example or even an initiative. Highlights -Meeting kicked off with “Tell me what AI application you are most excited about” and the response were Waymo and inbox cleanup. Really? Not a single internal, corporate example to highlight? -Databricks employee was great / honest. Comments suggested Agentic AI is in the early R&D stage – can write code for the agents but still very difficult to test them. Also made a very interesting point: code written on mainframes for a specific process is still running 40-50 years later. AI agent code has a half life of 3-6 months so need to be retrained frequently to keep them functional. [That makes sense to me. Think of how peoples’ job functions evolve even if ever so slightly over time – maybe you need to use a different portal, corporate policy changes on returns, etc.]. -Described AI agents as similar to children – can do some amazing things but also very unruly and can do crazy things. Agents also get more unruly the larger they get / more complicated task they attempt. -The customer representatives highlighted they are basically nowhere w/ AI Agents. AI zeitgeist spurred them all to consolidate / clean data so they can test AI. But the most tangible cases mentioned were simply document summarization / querying. This is just a point in time but does seem like AI Agents are nowhere close to working right now. The SaaS companies are all working on them, are arguably the best application coders out there, and are struggling to get it to work. PLTR is the only one that seems to have success but their approach doesn’t seem that agentic – they just go into a customer, collect all the data, and setup an application the customer can use (portal that tells you which planes need which parts to be replaced preventatively, etc.). Probably bullish for SNOW / Databricks as everyone continues to get their data in order, bullish for application SaaS, and probably bearish on margin for AI infra (ie. slow your roll).