How harness engineering, agentic systems, and domain-first businesses will accelerate AI adoption, unlock revenue, and shape the path to AGI
Most conversations about AI focus on the model. I want to focus on what increases AI adoption, revenue, and real-world reliability. I identify that to be scaffolding. Scaffolding is the layer of software, processes, agent hierarchies, edge-case engineering, and business design that turns foundation models into dependable systems people can use every day.
Thesis
I believe scaffolding will take us all the way to worldwide AI adoption, which will bring in the revenue needed to accelerate the path to AGI and the future of robotics. Scaffolding will not only increase global adoption but also accelerate higher-capability systems by creating real feedback loops, revenue, and incentives to improve model and tool reliability. My bet is on building scaffolds, shipping practical agentic systems, and owning domains where those systems deliver direct ROI.
What I mean by scaffolding
Scaffolding is everything that wraps a model and makes it useful in the wild. It includes:
- Orchestration between specialized agents that play architect, builder, auditor, and integrator roles.
- Connectors to enterprise systems like Jira, Confluence, GSuite, ERPs, CAD viewers, and domain specific file formats.
- Business logic for edge cases such as credit notes, refunds, alias emails, or proprietary file types like DWG.
- Monitoring, fallback, graceful exits, and credit/quota management so the system does not catastrophically fail when a model hallucinates or a bearer token expires.
- UX patterns that let humans review and approve drafts quickly so systems are useful while remaining auditable.
These pieces are less glamorous than the model. They are more engineering than research. They are where product meets operations.
Why scaffolding matters more than pundits admit
Foundation models are necessary but not sufficient. At the enterprise level no one trusts a single API call to produce a final legal, financial, or operational decision. Instead they orchestrate teams of agents that draft, challenge, audit, and synthesize answers.
Private agentic systems are a competitive moat. Large companies build internal agent ensembles and keep them secret because they replace headcount and become part of the secret sauce. That explains waves of layoffs in sectors where these internal systems work well.
Removing human noise yields outsized gains. Human judgment is noisy. Even weak algorithms reduce variance and improve performance by enforcing process. Scaffolding is how you make algorithms actually replace or augment humans without wrecking operations.
Developers and integrators are the adoption multiplier. Replit, Poetiq, and other platforms show how an architect agent, coder agent, and auditor agent can collaborate to produce far stronger outputs than a single pass LLM.
Anthropic and the aesthetic of design
Opus 4.5 put a simple idea on the table about humans preferring to suffer as someone than be at peace as no one. I use that insight as a case study for why some labs create culture and product that resonates beyond raw metrics. Anthropic, and small labs that mix a certain design mysticism (soul artifacts) with engineering, are my bet for products that will be trusted inside enterprises. Their approach to safety, behavior design, and product taste makes their models natural participants in well built scaffolds. Credit for the Opus 4.5 reflection goes to Adi (@adonis_singh) on X, who shared it on Nov 30 2025.
The product pattern I am building for
I am doing three things concurrently.
- Audit and instrument. I audit companies and their processes to map the exact flows humans take today. I instrument those flows with telemetry so failure modes surface quickly.
- Automate the low hanging fruit. Email replies, invoice generation, proposals, PDF and PPT manipulation, simple CRM updates and repetitive follow ups are where ROI is immediate. Ship these first and learn the edge cases.
- Move up the stack. After repeatable wins, we tackle higher consequence tasks. That requires tighter scaffolding, specialized validation agents, and business rules tuned to the domain.
Why I sometimes advise founders to build the domain before selling automation
Most companies I audit are intermediaries. They do not manufacture; they contact suppliers, send RFQs, assemble proposals, and chase approvals. That means a huge chunk of their value is simply coordination. If you are excellent at building scaffolding you have two strategic choices:
- Sell automation to those intermediaries and capture a slice of their productivity gains.
- Start the domain business yourself. Become a supplier, materials company, or construction outfit that uses your scaffolding to operate faster and more cheaply than competitors.
The second option captures first order ROI. Instead of splitting the value with a client you keep the revenue and use automation as a competitive advantage. That is what I am testing in parallel with the auditing and product work.
A practical example of agent orchestration
When you ask an integrated system to solve an operational task it should not be one shot. Instead it follows this loop:
- Architect agent breaks the problem into sub tasks.
- Worker agents generate drafts or outputs for each sub task.
- Auditor agents check compliance, formatting, and edge cases.
- Synthesizer agent consolidates the outputs and flags uncertainty.
- Human-in-the-loop reviews and approves or requests rework.
This is how I design flows. In code platforms you can see this in action. It is also how enterprises that succeed have built their private stacks.
Edge cases and why they are the real engineering
You only find many edge cases by running systems. Examples I repeatedly encounter:
- Supplier sends DWG or unusual CAD format. Do you convert to JPEG, fine tune a reader, or require human upload?
- How do you handle credit notes and partial refunds in invoice generation?
- What happens when an email arrives from an alias and the system thinks it is a different person?
- How do you ensure graceful degradation when the model runs out of credits or the API errors?
Solving these requires product discipline, instrumentation, and the patience to debug one case until it never returns. That is where scaffold builders earn their margins.
Business implications
- Faster adoption means more recurring revenue. Companies buy what reduces friction and produces reliable returns.
- Scaffolding is a defensible service and product. It is hard for competitors to replicate the exact integrations, the bug backlog, and the industry tacit knowledge.
- If you own a domain business that runs on your scaffold you win the full ROI and create a tougher economic moat.
Ethical and operational risks
Scaffolding concentrates power. Private agentic stacks can automate decisions that once required humans. That is useful and risky. To build responsibly you must handle data governance, audit trails, explainability, and clear human override paths.
How I am putting skin in the game
I am actively auditing companies and building agentic scaffolds that swallow existing workflows, instrument the edges, and replace manual repetition first. I am also experimenting with domain-first enterprises where automation is not a vendor feature but an operational advantage. That dual approach creates learning loops, client references, and a balance between product revenue and owner returns.
Call to action
If you are curious to test a scaffold on a real workflow, want an audit of your process, or want to discuss starting a domain business that uses automation as a moat, reach out. I will show the edge cases, and the metrics that demonstrate why scaffolding works.
Conclusion
The world runs on coordination. Emails, messages, documents, tracking, responding. Scaffolding connects AI to these workflows, handles the edge cases, makes it reliable. While others build better models, there's enormous value in making current models actually work in the real world.
The future arrives through scaffolding. Not just better models, but better integration, better handling of reality's messiness, better agentic architectures. The companies that figure this out won't just use AI. They'll be built on it from the ground up.
And that's the world I'm betting on.