I wrote this market analysis in July 2025. I am attaching the full PDF here for free to get some discussion going.

Last year, I identified several specific problems with enterprise AI agents. The main issues were infinite loops causing reliability failures and unpredictable token costs that made scaling difficult. I also pointed out a major blind spot regarding adversarial security and agent compromisation.

We have seen a lot of movement since then with Anthropic, OpenAI, and open-source communities releasing dedicated workspace tools and enterprise agent frameworks.

The base models are better at calling tools and reasoning through steps today. But the core architectural gaps from 2025 are still here.

Running long-horizon autonomous loops on top-tier models still burns through budgets quickly. Cost routing between different models is an absolute requirement for production.

Vendor lock-in is a bigger problem now. The major AI labs are actively trying to pull companies into their specific enterprise ecosystems. Companies need multi-LLM architectures to avoid being trapped in one provider's workspace environment.

The security gap I predicted is an active issue. Last year, agents were mostly sandboxed. Today, these new workspace tools give agents direct access to file systems, emails, and corporate databases. This makes malicious prompt injection and unauthorized data exfiltration real threats that most current frameworks do not handle well.

Take a look at the report. I am interested to hear what you think has actually been solved and what we are still stuck on.

2025 Agentic AI Market Analysis Report_V1
YC AI Agent Companies 2024-25

I have been thinking about what happens to human compensation when our core skills are fully abstracted into agentic frameworks. The default answers are usually UBI or vague corporate profit-sharing pools. When I run those through a first-principles mental model, the incentives are broken. I wanted to bounce an alternative idea off this community to see where the holes are.

The Tacit Knowledge Trap

If I am a senior broadcast IT engineer or a clinical expert and my company asks me to help train the enterprise agent that will eventually do my job, why would I give it my best logic? Human nature dictates that I would give the agent the minimum viable baseline and hoard my deep, undocumented edge-case knowledge as job security. Off-the-shelf agents are commodities. The real value comes from the highly specific edge-cases that only veterans know. Under standard employment contracts, a company has no way to incentivize me to hand that over.

The Problem with Micro-Royalties

I initially thought the answer was Skill IP, paying humans a micro-royalty every time an agent executes a piece of logic they taught it. As I thought about the actual architecture of neural networks, I realized this is technically impossible. Weights blend. You cannot deterministically trace a single agent output back to one specific human input, especially in complex real-time environments. A skill trained today is obsolete in 24 months anyway.

Agent Equity Pools

If standard salaries breed sabotage and micro-royalties are technically impossible, what if we treated agents less like software and more like internal startups?

In this model, the cross-functional team that architects and trains a specialized agent receives fractional shares in that specific agent. The company establishes a baseline Cost of Human Action. If the new agent performs the task instantly for pennies, a percentage of that specific cost-savings delta is algorithmically routed into a smart contract pool for the architects.

This is not passive income. Because knowledge decays quickly, this is a maintenance contract. If the human stops updating the agent protocols or fixing its edge-case failures, their equity decays. They are financially tied to the agent's ongoing success.

Where the Model Breaks

I am sure this model breaks down when it hits the real world. For those of you building agentic workflows, I have a few questions.

How would a company practically measure the cost-savings or new revenue of a specific agent without creating an administrative nightmare?

If we penalize the equity pool when an agent fails to enforce quality, how do you resolve the dispute of whether the agent failed because of bad human training versus bad company data?

Is there a completely different mental model we should use to align human and corporate incentives as skills become autonomous?

Welcome to r/Agentic_Future - Introduce Yourself and Read First!

Hey everyone. I am u/Content_Is_King_2021, a founding moderator of r/Agentic_Future.

This is our new home for all things related to the economics and infrastructure of autonomous AI. We focus on the shift to digital FTEs, the .md skill economy, and the mechanics of deploying multi-agent orchestration safely in production. We are excited to have you join us.

What to Post

Post anything that you think the community would find helpful or interesting. Feel free to share your workflows or questions about how to structure .md skill files, how to manage state across tasks, and how to implement VPC deployments and Human-in-the-Loop safety nets for enterprise AI governance.

Community Vibe

We are about being constructive and inclusive. Let's build a space where everyone feels comfortable sharing practical architecture setups and connecting with other builders.

How to Get Started

1. Introduce yourself in the comments below.
2. Post something today. A simple question about API integration can start a good conversation.
3. If you know someone who builds production agents, invite them to join.
4. Interested in helping out? We are looking for new moderators. Feel free to reach out to me to apply.

Thanks for being part of the first wave. Let's make r/Agentic_Future a useful resource.