Enterprises run on outcomes.
Outcomes are triggered by decisions. Every organization—whether it’s a startup or a global enterprise—runs on an endless stream of decision points: approve or reject, allow or block, ship or delay, hire or pass, escalate or ignore. Decisions move money, unlock access, trigger real-world actions, and create downstream effects that can compound fast.
So here’s a question worth asking:
If decisions are the lever that moves outcomes… why do we spend so much time optimizing models for “better responses,” but not enough time engineering systems for better decisions?
And here’s the bigger question:
Are we watching AGI evolve right now—moving from decision support toward decisioning systems, from narrow AI toward general intelligence?
Because that shift feels natural, inevitable, and—most importantly—scientific.
We didn’t develop AI by chance — we modeled our brains
We built AI on purpose.
Modern AI didn’t emerge randomly. We modeled parts of our brain’s infrastructure and translated biology into engineering:
- neurons became computational units
- synapses became weighted connections
- learning became iterative adaptation over experience
That’s why today’s AI is at the frontier of science: we created learning infrastructures inspired by the brain. We built machines that can absorb patterns and improve with data.
But learning is not the finish line.
Learning is the foundation.
Now it’s time to make the system function automatically in the world. And the capability that separates “a helpful tool” from “an autonomous system” is decisioning.
If machines can “do decisioning,” doesn’t that get us to AGI?
It gets us closer than most people admit—but it’s not automatic.
“Decisioning” is not one feature. It’s a capability stack. In my view, accurate decisions—whether made by humans or machines—depend on four major entities:
- Analytics (Explorative Intelligence)
- Stored Memory of past scenarios
- Identity of the decision maker
- Cognition that triggers the decision
And this model applies across entity types—from the best-known human intelligence to the systems we build today.
Let’s break it down in plain terms.
1) Analytics: Explorative Intelligence (the “what might be true” engine)
Analytics is exploratory. The more information you can explore, the more you can discover.
This is where GenAI and LLMs shine. They’re incredible at exploring: generating options, summarizing signals, reasoning across ambiguity, and producing many candidate paths. They can propose answers, strategies, and explanations at scale.
But exploration is not decision authority. Exploration gives you breadth. Decisioning requires commitment.
2) Memory: Stored scenarios (the “what happened before” engine)
Better memory leads to better decisions because it helps avoid the pitfalls of past experiences.
For humans, memory is lived experience.
For systems, memory is past outcomes, curated training data, labeled scenarios, incident learnings, and the pattern library of “what worked vs what failed.”
Without memory, systems repeat mistakes with confidence—and at enterprise scale, repeated mistakes become operational cost, safety risk, and compliance exposure.
3) Identity: The decider (the “what I stand for” engine)
Identity is the system that makes the decision and what it’s made of.
For humans, identity includes ethics, ideas, policies, and emotions. Even when we try to be purely rational, our identity shapes how we weigh risk, fairness, and responsibility.
For systems, identity is expressed as operational policies and constraints:
- what is allowed
- what is forbidden
- what requires escalation
- what must always be audited
- what should never happen—even if it’s “likely correct”
Identity matters because organizations don’t just need “accurate.” They need “aligned.” They need decisions they can defend.
4) Cognition: The trigger (the “decision ignition” engine)
Now we arrive at the missing piece.
Analytics, memory, and identity are pieces of a puzzle. But cognition is the part that initiates a cognitive function—like creativity, thought, and decision-making.
Cognition can be felt through the cognitive functions it performs: creativity, thoughts, decisions, judgment.
Humans have this naturally.
Most systems don’t.
Systems are solutioned through logic and code. They run workflows, execute conditionals, and automate steps—but they don’t truly have a cognitive trigger that converts information into governed action.
That’s why humans are autonomous—while many systems remain tools.
What we actually need: decisions + the chain of truth behind them
A decision is the outcome of a cognitive function.
But the decision alone is not enough.
What matters is the decision and the information chain that triggered it:
- the signals that mattered
- the memory that shaped it
- the identity constraints that bound it
- the cognition layer that initiated action
- the trace that makes it explainable and auditable
Because in the real world, decisions aren’t just made—they are challenged, disputed, reviewed, and audited.
EvaluatorDPT: a cognitive decision layer
This is why we built EvaluatorDPT at Simple Machine Mind.
EvaluatorDPT is a state-of-the-art AI system designed to make decisions using information, with:
- Policy as Code as a core feature
- Explainability as model outcomes
Put simply:
- Use LLMs for explorative analytics (generate options and insights)
- Use EvaluatorDPT as the cognitive layer to produce decision outcomes
- Use Agents to execute those decisions and complete the workflow
This is a practical architecture for building autonomous systems:
Exploration → Decisioning → Execution.
You can fit EvaluatorDPT into almost any workflow where decisions must be consistent, governed, and defensible.
Identity as a first-class primitive
We’re also building something ambitious at Simple Machine Mind.
Instead of sourcing the identity of a standard system, can we bring the identity of specific individuals?
Because it points toward a future where human cognition can be preserved beyond generations:
- enabling grandchildren to talk to a cognitive mirror of their great grandparents
- making distant travel more meaningful through preserved identity and decision style
- and even enabling humans to be placed into synthetic bodies that can talk and make decisions like the individual who created the mirror
That isn’t science fiction.
It’s a clear possible science—if done ethically, safely, and with the right governance.
We have a complete idea, a provisional patent direction.
Decision products today: Signals, and the road ahead
While we work through some interesting endeavors, we’ve already released decision products like Signals.
Signals can make real-time decisions—ranging from critical flight landing decision scenarios to decisions about whether a system is compliant and can function within the guardrails.
Our model currently has an F1 score of 82.4 and is still improving. This is early—crawling stage—not the final form.
Right now, the product is best suited for compliance-based decisions like Security, Recruitment and any workflows requiring decisions based on company policies or framework. Truly real-time, crucial decisions require more testing, validation, and adoption. But the direction is set.
The AGI path: exploration is here — decisioning is next
We all want to move toward AGI. Think about how humans evolved capability. It started with a simple evolution: sounds → words → language → exploration → decisions → Knowledge.
We are at this Language state today with AIs. Language Models.
Language models accelerate explorations.
Exploration enabled better decisions.
Better decisions created better outcomes and knowledge systems.
That arc isn’t magic. It’s evolution. And AI is moving through a similar progression.
GenAI gave us systems that can speak, write, reason, and propose. It gave us exploration at massive scale—systems that can generate gazillion ways to solve a problem.
But that creates a new challenge:
If you have 1000 ways… which one gets you closer to the outcome?
And can you select it reliably, under real constraints, with real risk, and real accountability?
That’s the bridge we’re crossing now. Exploratory systems are paving the way for decisioning systems.
Outcome is the hard part.
Now imagine a system that can take those 1000 ways and give you a definitive path—considering your goals, enforcing guardrails, and giving you a way to steer decisions toward the outcome before you commit execution.
That’s what we offer as Decision Steering.
Exploration is powerful. Decisioning is decisive. Steering is control.
We’re in Azure Marketplace — and we’re early, but serious
We’re available in Azure Marketplace.
And we’re an infant as a company—a startup—but ambitious. The product and solutions we’ve built exist because exploratory GenAI made it possible for a single builder to engineer systems that previously required teams.
This is exactly why people say AI can replace humans. But I see it differently:
AI doesn’t replace human value. It changes the shape of work—and raises the value of the builders and the leaders who can guide people through the change.
What tomorrow could look like
Here’s what I believe tomorrow will look like:
- Developers and ground workers become gold dust—the highest paid people.
- People leaders remain essential—leaders who can make people feel like winners.
- The scope for non-technical decision roles winds down, as knowledgeable systems and developer intelligence take over routine decisioning.
- No one is “out of a job”—the world shifts further into services. Humans will maintain systems that can do their work, and the work becomes building, governing, and improving those systems.
Closing questions for you
So I’ll end where I began—with questions you can apply to your own organization:
- Are your AI systems only exploring… or are they making governed decisions?
- When decisions are wrong, can you trace why—and defend the chain of truth?
- Do your workflows have a cognitive decision layer, or just automation around human approvals?
- If AGI is evolution, are you building the next step—decisioning—on purpose?
Because this transition is already underway.
Exploration is here.
Decisioning is next.
And what we build now will define the intelligence infrastructure of tomorrow.
Ready to bring Decisioning into your workflow?
If your organization is using GenAI for exploration, the next step is decision authority: governed, explainable, policy-bound decisions that can drive real outcomes safely.
EvaluatorDPT™ is Simple Machine Mind’s Cognitive Decision Layer that turns messy signals into actionable decisions (Yes / No / TBD) with Policy-as-Code and audit-ready explainability.
Where EvaluatorDPT fits
- Between LLMs and Agents: Explore with LLMs → Decide with EvaluatorDPT → Execute with Agents
- Directly inside workflows (no LLM required): plug EvaluatorDPT into compliance checks, approvals, content decisions, operational gating, and safety constraints
- Decision Steering: adjust outcomes toward goals while enforcing guardrails—before execution
Current benchmark
- Model quality: F1 Score = 82.4 (improving)
Explore EvaluatorDPT
About Simple Machine Mind
Simple Machine Mind builds decisioning systems for enterprises—so outcomes are driven by decisions you can defend. Our long-term direction includes HACM (Human AI Cognitive Mirror): an initiative to model identity as a first-class primitive for cognition-preserving systems, built with safety and governance at the center.
Notes & Disclaimer
EvaluatorDPT™ is a trademark of Simple Machine Mind.
We’re building toward AGI—engineered, not magic. This post focuses on the practical decisioning systems we can deliver today.