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Agent demos work. Agent production is a different sport.

·532 words·3 mins
Table of Contents

Angle: Deflates the demo hype. The demo star is the agent. The production star is boring infrastructure: durable execution, checkpoints, replay, traces. Controversial because it tells builders that the “cool” part is the easy part.

Template: purple cover + purple slides (Torchenstein brand)

Slide 1 — cover #

Headline: Agent demos work. Production is a

Highlighted keyword: different sport.

Designer note: White bold headline, yellow highlighted “different sport.” Cover template with Torchenstein.

Slide 2 #

Headline: In the demo, the agent is the star.

Supporting copy: It reasons. It calls tools. It solves the task. The audience applauds.

Designer note: Yellow text. Set up the contrast — this is the seductive illusion.

Slide 3 #

Headline: In production, the agent is 20% of the problem.

Supporting copy: The other 80%:

  • durable execution and checkpoints
  • replay after partial failure
  • human-in-the-loop pauses
  • structured traces and audit
  • service discovery across teams

Designer note: White text with bullet list. This is the reality check slide — hit hard with specifics.

Slide 4 #

Headline: Agent systems are long-running distributed processes.

Supporting copy: Not request-response functions. If your agent can pause, wait for approval, resume tomorrow, or retry one expensive step — you are already in workflow-runtime territory.

Designer note: Yellow headline, white copy. The reframe — boring but true.

Slide 5 #

Headline: Before building the next agent, answer this.

Supporting copy: Can your system: → Resume after a crash without repeating expensive calls? → Replay a failed step without re-running the whole chain? → Pause for human approval and pick up later?

If not, start here.

Designer note: Yellow headline, white arrows. Practical checklist to start with.

Slide 6 #

Headline: Model serving is a commodity. Durable execution is not.

Supporting copy: You can buy inference from any cloud provider. You cannot yet buy clean cross-agent identity, replayable execution, and org-wide policy control.

Designer note: Yellow headline, white copy. The “where the real value is” slide.

Slide 7 #

Headline: What breaks first when your agent system goes to production?

Supporting copy: Retries? State management? Observability? Permissions? That answer tells you where to invest next.

Designer note: White headline, yellow highlight on key words — closing CTA for discussion.

Companion post copy (text to accompany the carousel in LinkedIn feed):

Agent demos are dangerously convincing.

The agent reasons, calls tools, solves the task. Everyone applauds. Then you try to ship it.

Suddenly the hard part is not the model. It is everything around it: → What happens when the agent crashes mid-task? → Can it resume without re-running expensive tool calls? → Can it pause for human approval and pick up the next day? → Can you replay a single failed step without restarting the chain?

These are not AI problems. They are distributed systems problems. Durable execution, checkpoints, replay, idempotent side effects, structured traces.

The uncomfortable realization: model serving is becoming a commodity. I can buy inference from any provider. I cannot yet buy clean answers to cross-agent identity, replayable execution, and organization-wide policy control.

The best agent systems will not win on model quality alone. They will win on infrastructure nobody finds exciting to build.

What breaks first when your agent system moves from demo to production?

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