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A lot of companies are trying to hire AI like it is a new employee category.
They want the magical demo where an agent reads a ticket, opens five tools, makes a plan, does the work, updates the system of record, and politely disappears before anyone notices how much labor just got compressed.
Sometimes that demo is real.
A lot of the time, though, what the company actually needs is something much less cinematic and much more useful: better glue.
Not an autonomous digital coworker. Not a fake employee badge for a model. Just better ways to connect the boring systems the business already runs on.
Because most operational pain inside companies is not caused by the lack of an all-purpose AI agent. It is caused by fragmented tools, brittle workflows, missing context, manual copy-paste, stale approvals, and humans having to translate between systems that should have been talking to each other in the first place.
That is not an intelligence problem first. It is an integration problem.
And until more teams admit that, they are going to keep buying agent theater when what they really need is infrastructure with better manners.
The current market loves a story where AI becomes a tireless operator sitting on top of the stack.
Read Slack. Check Salesforce. Open Jira. Query the warehouse. Update HubSpot. Draft the email. Trigger the workflow. Close the loop.
On a slide deck, that sounds incredible.
In a real company, it immediately runs into the stuff that makes software annoying in the first place:
That is where the dream starts wobbling.
The problem is not usually that the model cannot produce text. The problem is that the business process lives across five weird tools, three historical accidents, two legal constraints, and one ancient internal service nobody wants to touch.
What teams describe as “we need an agent” is often just frustration with those seams.
And seams are glue problems.
This is the part I think people still underrate.
Inside most organizations, an enormous amount of work is not actually high-level reasoning. It is coordination.
Moving information from one place to another. Translating one format into another. Making sure a state change in system A gets reflected in system B. Pulling the right context into the right interface at the right time. Preventing humans from retyping the same thing in four different tabs.
That is where a lot of real inefficiency lives.
A sales team does not always need an autonomous agent that “owns pipeline management.” They may need cleaner enrichment, automatic note sync, and fewer dumb manual updates.
A support team does not always need an agent that “acts like a senior support rep.” They may need instant retrieval of account context, product state, prior tickets, and known workarounds without playing archaeology across six tools.
An engineering org does not always need an agent that “ships features end to end.” It may need better internal search, better runbook retrieval, better deployment context, and better automation around the repetitive transitions humans keep doing by hand.
That is glue.
And glue has a much better success rate than theater.
There is a reason integration work feels unglamorous: when it works, it disappears.
The right automation does not put on a performance. It removes a stupid step. It makes the next action obvious. It carries context forward. It reduces the chance of a human making the same avoidable mistake for the fiftieth time.
That does not make for a sexy launch video, but it does make companies meaningfully faster.
By contrast, a lot of premature agent rollouts add entirely new layers of fragility:
Now instead of fixing the original workflow, the team has built a probabilistic workflow interpreter on top of it.
Congratulations. The process is still bad, but it is now harder to reason about.
This is why so many agent pilots feel impressive in week one and strangely exhausting by week six.
The demo measured possibility. Production measured operational drag.
If you want to know whether a company needs an agent or just better systems glue, stop asking:
“How do we automate this entire role?”
Start asking things like:
Those questions usually lead to much saner designs.
Maybe the right answer is not an agent that “runs onboarding.” Maybe it is a workflow that assembles account details, contract state, provisioning checklists, and task ownership into one place so humans stop playing relay race.
Maybe the right answer is not an agent that “does incident response.” Maybe it is tooling that pulls logs, deploy diffs, recent alerts, runbooks, and ownership context into one operational surface fast enough that humans can make better decisions.
Maybe the right answer is not an agent that “manages recruiting.” Maybe it is better synchronization between applications, notes, scheduling, and decision trails so the process stops leaking time.
That is less magical. It is also how real leverage tends to arrive.
This is where I think practical teams are heading.
Not toward fully autonomous everything. Toward well-bounded assistants wired into real workflows with clear permissions, limited scope, and strong context.
That looks more like:
Notice what is happening there.
The system is still creating leverage. It is still saving human time. It is still using models.
But it is doing so in a way that respects the fact that real companies are made of constraints, not just prompts.
This is a much healthier pattern than tossing a model the keys to a tangled process and calling it innovation.
One reason I like the glue framing is that it forces teams to improve the environment instead of fantasizing past it.
To build good glue, you usually have to make decisions that are healthy regardless of AI:
That is all real operational maturity.
And once you do that work, yes, more capable agents become more realistic later.
This is the part the hype cycle skips.
Autonomy is downstream of legibility. If your company’s processes are not legible, the model is not going to save you from that. It is going to inherit the confusion and express it faster.
Better glue, on the other hand, can start paying off almost immediately because it reduces friction at the exact places where the system is already breaking.
Here is the question:
If we removed the model entirely, would this workflow still be obviously worth cleaning up?
If the answer is yes, you are probably looking at a glue opportunity.
That is good news. Glue opportunities tend to be grounded, measurable, and survivable. They improve real work whether or not the model keeps getting better every quarter.
If the answer is no—if the whole project only makes sense when you assume near-magical model reliability across messy systems—then there is a good chance you are buying agent fantasy instead of solving an operational problem.
That does not mean agents are fake. It means the order matters.
First make the workflow legible. Then connect the systems. Then automate the bounded pieces. Then, if it still makes sense, give the model a little more room.
Most companies are trying to skip to the last step because it is more fun to talk about.
Unfortunately, reality still charges interest on skipped systems work.
They will be the ones that quietly remove friction across the company.
The ones that make information easier to find. The ones that reduce state drift. The ones that shorten handoffs. The ones that automate the tedious parts without blurring accountability. The ones that improve the system enough that humans and models can each do the part they are actually good at.
That is the boring truth underneath a lot of AI transformation talk.
Most companies do not need autonomous agents.
They need better glue.
And honestly, that is a much more useful thing to build.