Most people still think of “AI agents” as glorified chatbots.
It has evolved in March 2026. We are now building PERSONAL humans. This “human” can work for you 24/7, and (in the near future) can interact with money and systems on your behalf.
This post is the first section of a broader series where I’ll cover:
Tech stack foundations (today)
Standards + protocol “routes” your agent will need to follow
Economics: objective functions, cost functions, incentives
Security (enterprise-grade, in plain English)
To get started:
1) Agents change the “interaction surface” of the economy
We won’t just have humans interacting with humans. We’ll have:
humans ↔ agents
agents ↔ agents
agents ↔ humans
That shift matters because incentives, verification, guardrails, and organisational workflows.
2) Agents can interact with money
This is the jump from “automation” to “agentic fintech”.
Today, you can already automate bank transfers. But the interesting edge is when an agent can coordinate end-to-end execution, and logistics — potentially without touching bank rails.
A simple example I used: telling an agent to buy a surprise birthday cake within a budget, have it delivered, pay via crypto rails, and book delivery logistics.
Whether you like crypto or not, the design question remains the same for enterprises:
If autonomous software can initiate actions and payments, what ability, and control?
The PERSONAL framework (8 components of a “digital human”)
OpenClaw becomes easier when you treat it is.
I created this simple acronym PERSONAL:
Physical, Executive function, Rules, Self, Operations, Nurture, Articulation, Learning (skills).
You know I like these ELI5 method of understanding new concepts.
P — Physical
Three common options:
Hardware: local machine (e.g., Mac mini, dedicated workstation)
Cloud: AWS / GCP / Alibaba etc.
SaaS: subscribe and someone hosts/manages for you
Enterprise linking about deployment model, data boundaries, and ops ownership.
E — Executive function (the “brain”)
This is your model layer — your LLMs.
I frame this as a choice between:
Closed source (e.g., commercial providers)
Open source (self-hostable models)
In practice, you’ll decide based on the jobs to be done (cost, latency, expertise).
R — Rules (security + law guardrails)
In economics, rules define boundaries for behaviour. In systems, “rules” becomes:
security boundaries
guardrails
policy constraints
legal constraints
Enterprise lens: if the agent can take actions (and especially transact), rules are non-negotiable. Why? You don’t want your system to get hacked, or to behave outside its intended authority. It is so easy to be hacked at this moment.
S — Self (personality + character)
This is the agent’s behavioural layer.
This looks like your:
system prompt
role definition
“how I behave” rules (This is literally calle the
soul.mdfile)
Enterprise lens: tone and behaviour aren’t just “UX” — they affect operational risks.
O — Operations (tasks + routines)
This is your “scheduled work” layer:
routine tasks
operational workflows
recurring runs (e.g., update a database every 9am)
This can be framed as business operations.
N — Nurture (heartbeat monitoring)
I separate nurture from operations.
Nurture is the agent’s “heartbeat”:
background monitoring
scanning
frequent status updates (e.g., every 15–30 mins)
Enterprise lens: this is where reliability and general check comes in: “Is the system healthy? What changed? What needs escalation?”
A — Articulation (how you talk to it)
This is the interaction layer — your UI.
Examples:
Telegram, Slack, Discord, WhatsApp
Channel matters as a way to easily interact with your PERSONAL human and give it commands.
L — Learning (skills)
This is the capability layer: what the agent can actually do beyond chatting.
I used the “Matrix download” analogy: skills are modules you plug in, download, or build yourself.
This where agents become a secret superpower. Skills become reusable components across teams and workflows.
What I’m covering next in this series
This episode is the foundation.
Next, I’ll go deeper on:
wallet / custody considerations
standards and protocol
the economics layer: what to optimise, objective functions, incentives
If you’re building enterprise-grade agents, the point isn’t novelty. We really don’t need that in enterprise systems. This is system design. A new design of how you run your operations, systems and how people interact with that said system. That looks like authority boundaries, monitoring, and incentives — before scale forces the issue.
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