AI Chat Isn’t the Endgame
We’re in a transition moment, for sure. The chat interface was a necessary first taste of AI, but it isn’t the main course. If you’re a creative professional or a founder building with AI, it’s worth seeing where this is actually headed, and why the obsession with chat windows is distracting from the true goal for AI.
Where We Are Now: Why Chat Falls Short
Chatting with LLMs feels helpful at times, but is antithetical to how humans work. We’re asynchronous creatures, hopping between programs and tasks.
Chat‑based workflows force us into single‑threaded conversations that don’t fit the messiness of real creative work.
- Non‑linear work: People don’t work linearly. We jump around, test, and iterate. Chat can’t keep up when it lacks full workflow context.
- Catastrophic forgetting: Even large context windows can misinterpret priorities or lose track of nuance.
- Lack of guidance: Chat windows can provide facts, but they often fail to keep users aligned to the bigger vision when each task is isolated. Reference diagrams or project trees are necessary, at the very least.
- Mental load: For many folks, effective communication is hard enough. Adding a chat layer increases cognitive overhead that saps focus away from the underlying goals.
The Emerging Pattern: From Chatbot to Workflow Copilot
We’re seeing the cracks in the wall, though. The logical home for AI isn’t in a chatbox, it’s inside your tools, working quietly, handling small tasks in real time with minimal context needed.
Old Pattern:
- Ask question
- Read answer
- Copy into tool
- Repeat
Next Pattern:
- State your goal: “Produce three 30‑second cuts, publish, invoice.”
- A squad of agents plan steps and call APIs.
- The system executes under your guardrails.
- You review and sign off.
The difference isn’t subtle. It’s the leap from manually copy‑pasting between windows to letting the system run the steps in the background.
Design Principles for the Agentic Era
As we shift to agentic workflows, these principles will separate the flaky experiments from usable tools with predictable outputs:
- Progressive disclosure: The system should default to quiet automation. The logs from the AI should be available on-demand for auditability, but should not distract from the focus of the project.
- Two‑click override: Any agent action should be pausable and reversible instantly. A user should be able to bring the project to it’s last current state before engaging the AI. This will prevent unnecessary billing and data loss
- Policy as code: The user should be able to hard‑limit spend, scope the full project, and apply creative constraints in version‑controlled files that agents must prioritize.
- Observability: Every call to an AI tool should be treated like a render job. Timestamped with the inputs and outputs stored.
How to Start Building
You don’t need to overhaul your pipeline overnight. Start here:
- Prototype a micro‑agent: Pick one headache, whether it be renaming layers, pulling transcripts, first‑pass audio leveling and wrap it in an agent loop. Don’t try to make the agent do everything. Give it a set of tasks as you would a human worker.
- Embed, don’t bolt on: Let the model live inside your tools via scripting, not in a separate chat tab.
- Measure the difference: Start a timer while doing the task with AI. Is it truly saving you time in the long run, or are you spending hours chasing the new shiny object?
Why This Matters Now
The ground floor for creative tooling continues to rise. Chat interfaces lifted it once; agentic systems will blast it through the ceiling.
According to Asana’s 2024 Anatomy of Work Index, the average knowledge worker spends 60% of their day on “work about work” (status updates, switching apps, chasing approvals) and only 27% on skilled creation.
Those are some scary numbers.
Folks who stay welded to the chat window will keep nudging prompts while competitors ship finished deliverables in the background.
If you liked this article, I cover the same topic on my podcast “Driven to Create” and you can find it here:
