What’s 🔥 in Enterprise IT/VC #478

Beyond Models: The Execution Intelligence Layer for Agents

Dec 27, 2025

Merry Christmas 🎄 and Happy Holidays.

This time of year tends to bring long retrospectives. But a few essays this past week stood out for a different reason: they put real historical context around where we actually are in the AI curve and where things are heading next.

Three in particular caught my attention: Ivan Zhao (Notion - Steam, Steel and Infinite Minds), Aaron Levie (Box - Jevon’s Paradox for Knowledge Work), and Jaya Gupta (Foundation Capital - AI’s Trillion dollar Opportunity: Context Graphs). From very different vantage points, they all converge on the same core idea: we are not just layering AI onto existing software. We are rebuilding how work, decisions, and enterprise systems function at their core.

A few shared themes emerged:

1/ This shift is structural, not incremental.

Enterprise software is being re-architected around AI-native systems, not upgraded SaaS.

2/ Context matters more than raw data.

The future isn’t just storing files or tickets, but understanding why decisions happen.

3/ Agents only become powerful inside rich context.

Workflows, intent, history, permissions, and outcomes are what turn agents from demos into systems.

4/ We’ve seen this movie 🎥 before.

Every major platform shift looks primitive until the right abstraction layer emerges. That’s when value creation accelerates.

5/ This is not a one-year hype cycle.

It’s a multi-year rebuild of how work gets done, who does it, and what enterprise software even means.

That brings me to the theme I’ve been thinking most deeply about over the past year: context. When we talk about context, we’re not talking about metadata or better prompts. We’re talking about decision-time understanding. Context is the combination of inputs, intent, constraints, history, permissions, exceptions, and outcomes that surround every real enterprise decision. It’s the difference between knowing what happened and knowing why it happened.

Most enterprise systems were built to store records. They were never designed to capture decision logic as it unfolds. That gap is now becoming the bottleneck for AI adoption.

This idea isn’t entirely new. We can look to the software development world for an early signal. Spec-driven development has emerged as one of the fastest-growing areas in AI, built around a simple insight: agents need explicit constraints to stay on rails.

In that world, specs become living documents. They encode intent, boundaries, and expected behavior, and they continuously evolve as agents act and learn.

What’s different now is scope.

Decision intelligence takes this idea beyond code and applies it to every enterprise workflow where agents interpret fuzzy human intent, make tradeoffs, and act in the real world. What’s emerging is a broader, more powerful abstraction: the Execution Intelligence Layer.

The execution intelligence layer sits between intent and infrastructure. It is where context is evaluated, decisions are made, actions are orchestrated, and outcomes are captured. It turns understanding into motion.

I wrote more about this in What’s 🔥 #464:

Specs are that policy layer in action. They don’t just apply to code, but can apply to every workflow where agents interpret fuzzy human intent and act on it. Once in place, specs become living documents that turn text into agentic action.

Aaron lays out the opportunity ahead and why context is bigger than we thought.

Jaya Gupta takes this one step further by naming the missing artifact. Agents don’t just need rules. They need access to decision traces that show how rules were applied in the past, where exceptions were granted, how conflicts were resolved, who approved what, and which precedents actually govern reality.

Agents don’t just need rules. They need access to the decision traces that show how rules were applied in the past, where exceptions were granted, how conflicts were resolved, who approved what, and which precedents actually govern reality.
This is where systems of agents startups have a structural advantage. They sit in the execution path. They see the full context at decision time: what inputs were gathered across systems, what policy was evaluated, what exception route was invoked, who approved, and what state was written. If you persist those traces, you get something that doesn’t exist in most enterprises today: a queryable record of how decisions were made.

and the kicker is this - why startups can win

Systems of agents startups have a structural advantage: they’re in the orchestration path.
When an agent triages an escalation, responds to an incident, or decides on a discount, it pulls context from multiple systems, evaluates rules, resolves conflicts, and acts. The orchestration layer sees the full picture: what inputs were gathered, what policies applied, what exceptions were granted, and why. Because it’s executing the workflow, it can capture that context at decision time – not after the fact via ETL, but in the moment, as a first-class record.

Together, these ideas point to the same conclusion: models don’t create leverage on their own. The execution intelligence layer does.

I’m seeing this across the board in a number of portfolio companies. As Aaron Levie put it, there is a much thicker layer above the LLM than was initially perceived.

The moat isn’t the query-response interaction. It gets much stronger when workflows are automated, when humans are pulled into decision-making, when exceptions are handled, and when systems learn from those outcomes to codify better behavior over time.

The last mile in the enterprise is always the longst. That’s exactly where the opportunity lies in 2026. As agent systems evolve into decision, execution, and learning engines, the value unlocked inside the enterprise will be massive. There is still a lot to build 🏗️ and fix to get there.

As always, 🙏🏼 for reading and please share with your friends and colleagues!

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Scaling Startups

#the mindset of a winning founder

#solid advice

#🎯 focus on the goal of showing up day to day, embrace the grind

Enterprise Tech

#🤯 Nvidia announced a $20B Inference Technology Licensing Agreement with Groq on Christmas Eve. Groq last raised $750M at a. $6.9B valuation just a few months ago and is now “selling” for $20B. Here’s one of the better takes amongst the many on this deal

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