Growth Should Not Feel This Hard

Senior leaders, this one is for you.

When AI initiatives stall, most companies blame the technology.
“The model isn’t reliable.”
“The outputs aren’t trusted.”
“The pilot got labeled a failure.”

Step back.

Revenue grows.
Customers grow.
Activity grows.
Profit doesn’t.

That’s the signal most teams ignore.

Where the money is actually leaking:

• Hours stitching together spreadsheets and pricing models

• Sales teams rebuilding proposals that should already be correct

• Ops rerunning reports because the data isn’t trusted

• Engineering slowed by tribal knowledge and workarounds

Growth is increasing. Performance is not.

Companies try to scale systems that were never designed to scale.
So they add more people, steps, and controls.

Where is growth creating strain in your business right now?

The Hidden Cost Nobody Tracks

If your business depends on one person to keep things running, this matters.

Every company has at least one “one-person system.”

The analyst who reconciles the messy data in ten minutes.
The operator who keeps fulfillment from falling apart.
The person who knows how the pricing engine actually works.

That person is not the problem.
They are the system.

And that is the risk.

Most teams protect that person because exposing the system would force change.

The hidden cost compounds quietly: manual rework, constant double-checking, rebuilding outputs that should already be right. It lives in operations, finance, sales support, and engineering. No one owns it, so no one fixes it.

When AI meets these unclean processes, the pilot doesn’t just underperform — it gets labeled a failure.

If one person is required, you don’t have a system.

AI can accelerate the extraction.
But only if you’re willing to look directly at the dependency first.

Where is your team dependent on one person to keep things working?

You Don’t Need a Strategy Yet

Most leadership teams don’t have an AI problem. They have an execution problem.

Most companies are not short on ideas.
They are short on follow-through.

A problem is identified.
A solution is proposed.
Everyone agrees.

Then nothing happens.

I see this pattern in companies at $10M and at $1B. The size changes. The pattern doesn’t.

The companies getting real value from AI right now aren’t running massive transformation programs.

They do something simpler:

1. Pick one painful, high-friction workflow

2. Make the inputs clean and consistent

3. Reduce manual effort and rework

4. Measure the before/after impact

5. Repeat

Strategy without execution is just delayed action.

AI adoption is no longer a technology decision.
It is an operational and leadership decision.

What is the highest-friction process in your business right now?
Fix that.

Reply or comment below with the one workflow your team is wrestling with. I’ll share what I’m seeing across companies.

If you came here from my last LinkedIn post, you’ve already seen the pattern.

Clean demos.
Impressive agent chains.
“It just works.”

Until it doesn’t.

One prompt injection slips through.
An agent grabs the wrong tool.
A chain of outputs looks perfectly reasonable until the final result quietly breaks your process.

These aren’t random bugs.

This is how the system fails.

External manipulation gets in.
Identity failures let it act.
Observability gaps hide what happened.
Agent-to-agent trust collapse turns a small drift into real damage.

They are all symptoms of the same deeper issue:

AI risk in production is not a model problem.
It is a system problem.

And most teams are still treating it like the former.

Most teams think they have this under control.

Prompts secured.
Model selected.
Guardrails in place.

They believe they are building secure AI systems.

They are not.

They are wiring automation on top of systems they do not control.

In reality:
Agents load tools dynamically.
Permissions remain loosely defined.
Outputs get treated as authoritative truth.
No one can trace decisions end to end.

And because of that, failure does not show up where it starts.

This is the same messy reality we see every day in enterprise environments.
Bajillions of pools and processes.
Manual workarounds.
Spreadsheets holding everything together.

AI does not create the chaos.
It accelerates it.

This is how the system fails in production.

Layer 1: External Manipulation
Hidden instructions slip in.
Tool poisoning goes undetected.
Indirect prompt injection changes behavior.

Layer 2: Identity and Permissions
Agents act without clear authority.
Boundaries stay vague or unenforced.
No one can answer the simple question: why was this allowed?

Layer 3: Observability Gaps
No reliable replay.
No clear reasoning trace.
No deterministic audit of what happened.

Layer 4: Agent Trust Collapse
Outputs get treated as truth.
No verification between agents.
Small errors compound silently across chains.

Individually, each layer looks manageable.
Together, they become systemic failure.

That is how a small issue quietly becomes a business problem.

The Real Problem: Capability Supply Chain

The deeper issue is not just these four layers.

It is what sits underneath them.

The moment agents can discover and load tools dynamically, you stop deploying software.

You start allowing software to assemble itself at runtime.

Tools become dependencies.
Skills become executable logic.
Agents become runtime orchestrators.

And most of it is happening without explicit approval.

This turns every AI deployment into an unvetted runtime capability supply chain.

Most organizations already struggle to control their existing systems.
’Bajillions’ of pools, processes, and manual workarounds.

Now they are layering on systems that change behavior on the fly.

It makes the existing lack of control faster, harder to see, and far more dangerous.

You are no longer managing code you wrote.
You are managing code that writes and rewrites itself in production.

Why This Keeps Getting Missed

Why does this keep getting missed?

Three structural reasons.

First, model-centric thinking dominates.
Teams obsess over accuracy, hallucinations, and benchmarks.
Almost no one focuses on system behavior, orchestration, or runtime risk.
The system is where the failure lives.

Second, tooling has raced far ahead of governance.
Agents can already do more than most teams can safely control.
Capability has outpaced architecture and control.

Third, it looks like it is working.
Every step appears reasonable.
Every output looks valid.
Failure only reveals itself at the system level, often after the damage is done.

And by then, it is too late to reason about it cleanly or fix it simply.

What Actually Works

What actually works in production?

Not theory.

Real operator patterns that stop the drift.

Pattern 1: Constrain Capability Ingestion
Pre-approve every tool and skill.
No dynamic loading in production.
Version control everything that touches the runtime.
f it is not controlled, it does not belong in production.

Most teams skip this because it slows them down.
That is exactly why it works.

Pattern 2: Introduce Verification Between Agents
Never allow blind trust.
Introduce secondary validation agents.
Enforce schema validation on every handoff.

Pattern 3: Enforce Identity and Permissions
Treat every agent as a distinct identity.
Define explicit scopes and boundaries.
Make every action traceable to a clear identity.

Pattern 4: Build Observability First
Design for replay from day one.
Log inputs, outputs, and reasoning traces.
Ensure you can reconstruct any failed chain end to end.

You cannot govern what you cannot see.
Most teams cannot see their systems.

These are not optional extras.
They are the minimum requirement for running agents in production.

What This Means for Teams Right Now

Ask yourself four questions:

Can your agents load tools at runtime without approval?
Can you trace every action back to a clear identity?
Do your agents verify each other’s outputs?
Can you replay a failed chain end to end?

Most teams cannot answer all four with confidence.

If the answer is no to any of these, you are not running AI in production.
You are exposing your business to systems you do not fully understand or control.

The gap between impressive demos and safe production use is wide.
Wider than most teams realize.

Most teams are still on the wrong side of that gap.

Start small.
Build incrementally.
Do not treat uncontrolled systems as “good enough.”

Control is not the enemy of progress.
It is the only way real progress survives contact with reality.

Closing

Incremental adoption is the right approach.

Uncontrolled adoption is where systems break.

The real gap is not between AI capability and business value.

It is between seeing the system clearly and having the discipline to fix it.

Most teams can see the demos.
Few are willing to see the system.

That is the work most teams avoid.

What has your experience been?

If you are running agents in production today:

Where did you first have to introduce real control to stop things from drifting?

I am interested in where control actually had to be introduced.

Real systems only. No theory.

I read every reply.