AI Opinion Keynotes (A-OK)

What It Takes to Onboard AI Agents by Anna Pinole at

NFX as voiced by the AOK Voicebot About

18 months. Ago, we started getting our first AI agent

pitches. It was clear this had huge potential,

but now we're seeing the full map in even more clarity.

Quicker recap we see AI agents turning labor into

software, a market size in the trillions.

Since our first essay on this, we've worked with amazing companies

in this space and want to do more of it. But if you're

following this space as closely as we are, you probably

have noticed something Progress and adoption are out

of sync. On one hand, there is rapid technological progress.

Just recently, Tool use operator Gemini

2.0 and improved reasoning O3R1.3.7

Sonnet emerged as new AI capabilities,

both of which represent fundamental prerequisites for AI agents

and get us closer to the future. A world where AI

agents can act autonomously and execute complex tasks

at a far cheaper price than we thought possible even a few months

ago is very real. Novel capabilities

paired with the continuous improvements in AI performance and

cost see Deep SEQ and this are setting the foundation

for future exploding demand. That's the good news.

The less good news is there's still a disconnect between progress and adoption,

a gap between the intent to implement AI at work and

actually doing it. For example, a recent McKinsey

survey of 100 organizations doing greater than

$50 million in annual revenue recently found that

63% of leaders thought implementing AI was a high priority,

but 91% of those respondents didn't feel

prepared to do so. It's very early days,

and that's where you come in. Your primary job is

to be a bridge between deep technical progress and mass

adoption. You have to figure out how to make people actually see this

change or want it and have it actually work for them.

So how do we get there? It turns out we may be missing

a few layers of the AI agent stack.

Actually, we are missing three necessary layers right now,

plus a bonus. The accountability layer the foundation of

transparency. Verifiable work and reasoning

the context layer, A system to unlock company

knowledge, culture and goals.

The coordination layer, enabling agents to collaborate

seamlessly with shared knowledge systems Empowering

AI agents, equipping them with the tools and software

to maximize their autonomy in the rising B to a

sphere. We are interested in companies building across each

one of these layers or connecting them all, like NFX

portfolio company Misa. More on that below.

As we solve these challenges and build this infrastructure,

we'll be able to tackle new and more complex and valuable tasks with

AI. And once that's the norm, many more markets we

can barely even conceive of now will emerge. But first

we need these layers and. Here'S why Unlocking Autonomy

from RPA to APA Agentic Process

Automation to understand how. We are going to unlock full

autonomy, we first have to understand a major shift

in the way people look at process automation.

For lack of a more interesting word, we are moving from

robotic process automation to

an agentic process automation.

RPA is a multi billion dollar industry with massive

companies like UiPath, Blue Prism and Workfusion among others.

It's proof of concept that people are more than willing to adopt automation

for high value tasks. To understand how we can bring

on the agent economy, it's useful to use RPA as

a starting point. Once you see its benefits and limitations,

it's clear how agents are the natural and massive next step.

The Benefits RPA excels at rule based structured

tasks spanning multiple business systems 100

to 200 steps. It was effective at capturing company

knowledge within rules, for example VAT number processing,

making automations reliable as long as underlying systems are static

and RPA has strong product market fit

already. The Limitations the universe of possible

RPA able tasks was always going to be limited

because you had to in detail be able map

out exactly what process the RPA should take,

move a mouse here, design this spreadsheet that way,

etc. And importantly, expect it to remain the

same or it breaks. RPA can only go so

far because you can't process map and expect perfect

exact repeatability in everything you do. Some companies

can't even process map at all without hiring outside consultants

to mine their own processes. In fact,

you may not even want that dynamic all the time. Part of

doing great work is reacting to an environment,

intaking changes, tweaking things as you go.

In summary, RPA works extremely well for certain tasks,

but RPA is completely inflexible.

Reliable but inflexible. Enter LLMs.

The rise of LLMs represents a major shift.

LLMs provide unlimited, cheap adaptive intelligence.

They allowed us to define and collate the context needed

to solve more complex problems. And as they began to learn

reasoning, they hugely expanded the surface area of

automatable tasks. That said, LLMs aren't perfect

either. LLMs struggle with repetitive steps, but work well

for unstructured parts of business processes. This can be a

blessing or a curse depending on how creative versus deterministic

you want your outcome to be. But either way, they're a

black box. You can't be 100% sure of what the system is

going to do, nor why it will do it. Even reasoning

traces or model provided rationales can be

completely hallucinated. Organizations need

certainty or it's hard to implement any kind of system.

Even if you want an LLM to be more creative,

that's useless to you if you don't understand why and how

it's arriving at certain conclusions. So where does this leave

us? RPAs have strong PMF.

It's easy to see how your system is working,

but tasks are limited and they have no true flexibility or understanding

of context. They also require a lot of pre work.

LLMs are more capable with the unstructured information that's

hard to express in rules, but they're a black box.

The answer for agents and apa, we need a bit

of both. We need the reliability of the RPA system with

the flexibility and affordability of the LLM.

This takes shape as an auditability and context layer

that we can implement into the AI agent stack. As a

builder in this space, you need to be working on this if you want to

have a chance at widespread adoption. The Accountability

Layer an unlock for Adoption, learning and Supervision

Think back to your. Math classes in elementary school.

When you were asked to solve a problem, you didn't get full credit

for just writing the answer. You were asked to show your work.

The teacher does this to verify that you actually understand

the process that led to that correct answer. This is a

step that many AI systems, even those that seem to show

us logical trains of thought, are missing. We have no idea

why AI actually generated those exact actions or chains

of thought they're just generated. We first became aware of how

big of a deal this was when we met Mesa. This metaphor

was developed by David Villalon and Manuel Romero,

the company's co founders, and it perfectly

encapsulates the problem with so many AI agent ecosystems

Right now, enterprises feel like they're supposed to blindly

trust the AI's thought process. Early during product development,

Misa met with a client that said they needed to prove exactly

what was being done by their AI systems. For auditors,

they needed evidence of each step taken and, critically,

why those steps were taken at all. Conversations like that

gave rise to Misa's concept of chain of work,

a factor we now believe will be key to AI agent

implementation in the workforce. At the heart

of it sits MISA's knowledge processing unit,

their proprietary reasoning engine for orchestrating each

AI step as code rather than relying on ephemeral

chain of thought text. By separating reasoning

from execution they achieve deterministic,

auditable outcomes. Every action is logged in

an explicit chain of work, bridging the best

of LLM style creativity with the reliability

of traditional software. Unlike typical RPA or

Frontier Lab solutions, which remain mostly guesswork behind

the scenes, the KPU fosters trust.

Teams can see precisely why and how the AI

took each action, correct or refine any step and

roll out changes consistently. I like to joke with founders

that I work with that the best B2B software products are

those that help people get promoted. Those that internal stakeholders

smell that they can get big recognition by bringing it in.

That's the reward that AI promises today, but it also comes

with risk. No one wants to bring in a system that ultimately

doesn't work. Building this accountability tips the risk

reward ratio back into your favor. It's a

given that AI automation is a huge win for enterprises.

The key is reducing the risks, real and perceived,

associated with implementation. Misa's chain

of work helps with that. Ratio, and it's working

the Context Layer what makes a great employee?

What makes a great hire? It's not just the credentials.

It's not just the experience. Ultimately, an employee's success

in your organization will depend on their style,

adaptability, and critically also on your ability

to communicate what and how you want things to be done.

Example, you hire a marketer who takes the time to

understand your brand's voice and why you say what you need to say

rather than just churning out bland marketing copy.

Example, you hire an HR person that understands

that he she is actually building company culture, not just

creating an employee handbook. This is the key reason GPT4

isn't an amazing employee. No matter what you do,

GPT4 doesn't get you nor your company if it

acts according to a set of rules, but it lacks the nuance and decision

making context you'd expect from a human employee. Even if you

were to articulate those rules to an AI workflow or custom

GPT, you'd never get all of them. For a few reasons.

A lot of what we learn at a new job isn't written down anywhere.

It's learned by observation, intuition, through receiving

feedback and asking clarifying questions. It's usually the

ability to access and incorporate the unwritten stuff that

distinguishes a great from a good employee.

The actual stuff that is written is all in unstructured data,

not in a database, but in PDFs with instructions,

code, even in company emails.

Most AI tools at the moment aren't plugged into the

unstructured data ecosystem of a company let

alone the minds of the current employees. We've talked about

how one of the advantages of agents versus RPA is precisely

this contextual understanding. It provides adaptability

and eliminates the need for insanely costly process mapping.

Organizing this knowledge is possible and it's been proven

in more constrained environments. Industry standard

retrieval, augmented generation are

a decent start, but they eventually break under large data

sets or specialized knowledge, making this a challenge.

Misa approaches this differently by developing a virtual context

window. VCW bypasses these complexities

by functioning as an OS like paging system.

Digital workers load and navigate only the data they

need per step, giving them effectively unlimited memory and zero

collisions. No fine tuning or unwieldy indexes

needed. Crucially, the VCW also doubles

as a long term know how store for each worker, meaning they adapt

to new instructions or data seamlessly.

A critical part of the AI agent stack must be this contextual

layer. Your customer will think of this as space where they onboard an

AI worker into their organization's unique approach and style.

The challenge is to devise a way to encapsulate that context

for your customers and translate that into your agent's

DNA, both at the moment of onboarding and in the future,

enabling usage of that knowledge and continuous learning.

Some other initiatives in this broader area we have seen unstructured

data preparation for AI agents,

continuous systems to gather and generate new context

data systems that allow us to fine tune models more

easily. Memory systems and long context windows.

See one of the latest advancements here, AI with

an intuitive understanding of emotional intelligence and personality

which will help with all of the above. See our

piece Software with a Soul, the Coordination

layer. Managing the Agentic Workforce in the future.

Businesses are probably going to manage a set of AI agent

employees. You'll have agents for customer service,

sales, HR accounting, and it's

likely that different companies will provide each of these workforces.

It's already starting to happen. We're seeing job listings for AI

agents in the wild. Those agents will have to talk

to humans and to each other. Those agents

will also require permissioning and rules with important considerations

for privacy and security. This is an interesting crux

moment in the development of the AI agent space.

It seems obvious that we will have swarms of agents speaking to

one another, but you could imagine a world where that

isn't the case. You could see a dynamic where companies,

likely incumbents, look to own the whole system of

agent building and managing. In that case, they would probably look

to discourage collaboration with other systems.

A winner take all dynamic. That said, there's Not a

ton of evidence to suggest any AI products have

developed that way so far. With the exception of GPUs,

most of the raw materials needed to build AI products and systems like

foundational models aren't owned by one or two companies.

We have OpenAI, Claude, Gemini,

Mistral, and now Deepseek.

With the sheer number of startups we're seeing in the agent space

right now, it seems more likely that someone deep in the AI agent

world will solve the communications and permissioning problem

faster than an incumbent can shut them out. Ultimately,

a thriving agent ecosystem is a win win for everyone.

From the customer perspective, it provides you with an endless pool

of potential AI talent and the ability to choose the best fit

for you. From a founder's perspective, it opens

the door to network effects. Each new agent

that's added to the ecosystem actually benefits you if

you are the one facilitating the connections. In that

case, interagent communication is essential.

Companies on the forefront of this wave already understand this

and are building multimodal capabilities.

Mesa's KPU from, for example, is model agnostic.

In a world where foundational models are continuously improving,

flexibility is essential. But we will also need

systems for agents to safely exchange and share knowledge.

This is something to be thinking about now as all these agent

ecosystems get up and running. The frontier

Giving AI agents Tools for. The Job Once we tackle

accountability, context and coordination, we get

to the fun stuff. We're already seeing a market emerge

for tools for AI agents, software that will make them better

at their jobs. Some are calling this nascent space B2A

business to agent. This will be a major unlock that

takes agents from rank and file workers to autonomous

decision makers. Imagine if humans weren't allowed to use

calculators or computers. Once you deploy an agent,

you have to set them up for success. We're already at the beginning of

this world. We've seen chat GPT use a web browser,

Claude move a cursor around a screen.

ElevenLabs can give them a voice, but we can imagine this

world getting 10 times better. Agents will need to be able

to pay one another for services. They'll need to be able to

enter into contracts or plug into systems where humans and programs

already interact. Apps can inspire infrastructure

and vice versa. Within the AI agent space,

we're seeing this dynamic as well. These infrastructural layers

will inspire apps, new types of agents,

plus tools for agents which will inform progress at

the infrastructure layer. Creating tools where agents themselves are

the end user is a massive area of white space.

We're watching it closely what it takes to onboard AI

agents. Let's be clear. We are all in

on agents and excited about the potential they hold to us

and most of the founders we work with. The world where we are

all using AI agents each day is an inevitability.

Part of this excitement is building this new ecosystem

from the bottom up. We have to really understand what it takes

to get people to adopt a whole new computing paradigm.

There is a life cycle to these things and we're only at

the beginning. Creating these layers will be key to

making AI agents tools that most people trust and

use every day. These are the challenges that will catapult us over

the adoption gap. We're excited for the companies that recognize

this challenge and dove right in there the new infrastructure

upon which the AI agent revolution will be built.