Most technical discussions around AI still focus on one question: how much faster can it make existing work?
That is useful, but incomplete.
I (Rizwanul islam Afraim) recently published a paper on SSRN arguing that agentic AI should not be understood only as a productivity tool. It should be understood as a coordination-layer technology.
Paper:
Agentic AI as Coordination Infrastructure Technology: Structural Implications for Firms, Growth, and Economic Divergence
https://ssrn.com/abstract=6236898
The distinction that matters
A lot of teams still think in this progression:
- Generative AI = outputs
- Copilots = assistance
- Agents = better automation
I think that framing is still too narrow.
The deeper shift is this:
When an AI system can decompose a goal, choose tools, execute multi-step actions, route decisions, monitor results, and iterate, it is no longer just helping produce outputs. It is starting to participate in coordination.
That changes how we should think about systems.
Why coordination matters
Most real-world systems are not bottlenecked only by raw task execution.
They are bottlenecked by:
- approvals
- handoffs
- status tracking
- supervision
- exception routing
- documentation
- compliance checks
- context movement between tools and people
That is why I frame agentic AI as Coordination Infrastructure Technology (CIT).
The argument is that its real structural impact comes from reducing coordination costs across workflows.
What this means for builders
If this framing is right, the important question is not just:
“How do I add AI to this feature?”
It becomes:
“How do I redesign this workflow now that coordination itself can be compressed?”
That leads to different design choices:
- fewer human handoff points
- more event-driven workflow logic
- stronger observability requirements
- clearer escalation paths
- tighter tool integration
- higher importance of permissions and guardrails
- more explicit human override layers
Why this is not just a product feature question
Once coordination gets compressed, system architecture starts influencing org architecture.
That means AI is no longer just a UI enhancement or backend helper. It starts affecting:
- team shape
- process design
- management load
- internal tooling strategy
- capital allocation toward compute, orchestration, and monitoring
That is why I think technical teams should look beyond prompt UX and model quality alone.
The bigger opportunity is in workflow architecture.
Final thought
The most important builders in the next phase of AI may not be the people who simply use AI tools well.
They may be the ones who can redesign systems around reduced coordination cost.
That is the layer I tried to explore in the paper.
Full paper:
https://ssrn.com/abstract=6236898
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