🧩 An AI agent today is a highly motivated intern with 400 tabs open.
on May 23, 2026
He responds quickly. He has confidence in himself. He knows how to use Gmail, Slack, Notion, GitHub, Linear, the terminal, and sometimes even Docker.
And yet, as soon as he is given a slightly longer task, everything starts to go haywire.
Not because the model is bad.
Because the system around it is chaotic.
The agents' current fantasy
The demo is always the same:
“Look, my agent can book a flight, send an email, create a Jira ticket and deploy to production.”
Technically, yes.
But in reality, most agents look like this:
- they open too many tools
- forget the initial context
- perform actions in the wrong order
- hallucinate intermediate states
- improvise when they don't know
- and most importantly: they don't know when to stop.
The problem is no longer:
“Does the model know how to do it?”
The problem becomes:
“How do we properly orchestrate our actions?”
LLMs are strong. The systems around them much less so.
ChatGPT, Claude, Gemini, or Qwen are capable of:
- summarize documents
- write code
- understand natural language
- reason in several stages
- to handle tools.
But an agent isn't just an LLM.
It is :
LLM
+ mémoire
+ outils
+ permissions
+ orchestration
+ validation
+ logs
+ contraintes
+ checkpoints humains
And today, most AI stacks still look like this:
“Connect a bunch of APIs together and hope it works.”
Orchestration has become the real problem
Imagine an orchestra without a conductor.
The musicians are excellent individually:
- very good violins
- very good battery
- very good brass instruments.
But nobody decides:
- of the tempo
- entrances
- transitions
- priorities
- of the end.
Result : It makes noise.
That's exactly what happens when you stack things:
- prompts
- tools
- MCP servers
- APIs
- autonomous agents
- workflows
- memory systems without defining:
- Who decides what
- in what order
- with what right of veto
- and with what limitations.
Unconstrained autonomy mainly produces chaos
An AI agent without safeguards is rarely “Jarvis”.
It's more like:
- an apprentice who has access to production
- without checklist
- without supervision
- without any notion of risk
- with a great deal of confidence.
The most dangerous thing is not that he might be wrong.
The most dangerous thing is that:
His mistakes often seem plausible.
He answers confidently. It generates credible plans. He explains his decisions.
Even when he makes it all up.
What we call “agent” today
Many current “agent” systems are actually:
- prompt loops
- tool wrappers
- routers
- call chains.
Very few actually possess:
- a stable memory
- a hierarchy of decisions
- a strategy
- a stopping capacity
- Clear permission management
- a deterministic orchestration.
We worked extensively on raw intelligence.
Not enough:
- control
- coordination
- supervision
- traceability.
True AI scaling may no longer be the model
For years, AI scaling mainly consisted of:
- Increase settings
- increase compute
- Increase the datasets.
But the next problem is more like this:
“How to properly coordinate probabilistic systems.”
In other words:
- orchestration
- runtime
- memory
- verification
- hierarchy
- constraints
- auditability.
The model becomes almost:
one component among others.
Building a useful agent is more like building an organization
A good agentic system looks less like:
“an omniscient AI”
and more at:
“a well-managed team”.
With :
- separate roles
- clear boundaries
- human validation
- stock journals
- budgets
- stopping rules
- supervision
- controlled retry
- sandboxing.
The problem is not only cognitive.
It is organizational.
From text generation to process generation
The real shift might be there.
The first LLMs generated:
- of the text
- of the code
- images.
The next systems will generate:
- workflows
- plans
- decisions
- executions
- complete pipelines.
And the more autonomous these systems become, the more:
- the orchestration
- the constraints
- the safeguards
- and traceability
become critical.
Build Toutouexplique
This weekend, we built a Symfony engine capable of transforming:
- a YAML topic
- a narrative structure
- a grammar of style
- rendering constraints
in:
- voice
- images
- scenes
- final video
- article.
The most interesting thing was not the generation itself.
The most interesting thing was:
the formalization of the structure.
We were not trying to copy a creator.
We were trying to understand:
- its rhythm
- its density
- its narrative structure
- its information compression
- his pacing.
In other words:
to transform an implicit grammar into an executable system.
The future of AI content will be structural.
Today, almost all AI tools can generate content.
The real differentiator becomes:
- the format
- the rhythm
- the orchestration
- consistency
- the structure.
Not only that:
“Is the model intelligent?”
But :
“Does the system know how to coordinate itself?”
And that applies equally to:
- AI agents
- video pipelines
- autonomous systems
- than for human organizations.
You have to dose
A useful agent is not necessarily the most autonomous.
It is often:
- the one with the best limits
- the best validations
- the best orchestration
- and the fewest tabs open.
Raw intelligence helps.
But without structure: Intelligence cannot be scaled.
We're just scaling the chaos faster.