From Observability to Intent: Scenario-Driven Systems and the Future of Software and Agents

January 29, 2026

A White Paper on Story-Native, Workflow-Driven Execution

Abstract

Modern software systems generate unprecedented volumes of data, yet remain increasingly difficult to understand, control, and trust. Observability has improved our ability to see what systems are doing, but it has not solved the deeper problem of comprehension: aligning system behavior with human intent, especially in an era where increasing amounts of code and logic are produced and operated by AI agents.

This paper introduces a new execution paradigm: Scenario-Driven Systems. Instead of reconstructing meaning from raw telemetry after execution, engineers declare Scenarios—structured templates of intent—before execution. These scenarios live inside Workflows and define what matters, what should be observed, and what success or failure means. At runtime, telemetry binds into these scenarios to produce Stories: bounded, causal, human- and machine-readable accounts of what actually happened.

Rather than treating narrative as a visualization or summarization layer, this approach makes story a first-class execution object. The result is a new control surface for software and agent-based systems: intent-first, structured, inspectable, and built for supervision rather than archaeology.

1. The Comprehension Problem

1.1 We Have Data, Not Understanding

Modern systems produce:

  • Logs
  • Metrics
  • Traces
  • Spans
  • Events
  • Snapshots
  • Streams

We can see almost everything.

Yet when something goes wrong, teams still ask:

  • What actually happened?
  • Why did it happen?
  • What was the system trying to do?
  • Where did behavior diverge from intent?

The problem is not visibility.

The problem is sensemaking.

1.2 Observability Is Necessary but Not Sufficient

Observability answers: "What signals can I inspect?"

It does not answer:

  • "What story just occurred?"
  • "What was supposed to happen?"
  • "Where did it go wrong?"

2. How Humans and Systems Actually Understand Things

2.1 Understanding Is Temporal and Causal

Humans do not understand systems as:

  • Static state
  • Or infinite streams of data

We understand them as arcs:

  • A beginning (intent, trigger, initial state)
  • A middle (decisions, actions, interactions)
  • An end (outcome, success, or failure)

In other words: stories.

Postmortems are stories. Incident reviews are stories. Debugging is the act of reconstructing a story from fragments.

2.2 Agents Also Operate Over Arcs

Modern agent systems already use:

  • Goals
  • Plans
  • State
  • Memory
  • Tool calls
  • Evaluation loops

They operate over sequences of actions over time toward an outcome.

They do not "understand stories," but they operate over the same underlying structure:

Intent → Execution → Outcome

3. The Fundamental Mismatch

Today we have two parallel worlds:

Human world:

  • Intent in tickets, docs, plans
  • Understanding in narratives

Machine world:

  • Execution in code, configs, infrastructure
  • State in logs and traces

Behavior emerges in between.

No one owns the full causal model.

This split is the root of comprehension debt.

4. The Core Thesis: Declare Intent First

4.1 The Traditional Model

Execute → emit data → reconstruct meaning

This is:

  • Expensive
  • Incomplete
  • Error-prone
  • Fundamentally archaeological

4.2 The Scenario-Driven Model

We propose the inversion:

Declare Scenario → execute → Story is instantiated

In this model:

  • Engineers declare Scenarios before execution.
  • Scenarios define:
    • What matters
    • What phases exist
    • What variables should be captured
    • What success and failure mean
  • At runtime:
    • Telemetry binds into the scenario
    • The result is a Story

The story is not reconstructed.

It is instantiated.

5. Terminology and Object Model

This paper uses a precise set of terms. These are not metaphors. They are concrete system objects.

Storyboard

The workspace and organizational surface. A Storyboard contains and organizes multiple Workflows. It is where engineers author, browse, and reason about system behavior.

Workflow

A Workflow represents a coherent domain of behavior such as "Checkout," "Cleanup Operations," or "Draft Management." A Workflow groups related Scenarios that describe how a system behaves in that domain.

Scenario (Template)

A Scenario is a declared, parameterized template of intent. It defines:

  • Phases
  • Variables
  • Expectations
  • Success and failure conditions

Scenarios are written before execution. They are schemas for meaning.

Events

Events are runtime telemetry: traces, spans, state transitions, logs, etc. They are raw evidence of what occurred.

Story (Instance)

A Story is not authored. It is instantiated.

A Story is produced when a Scenario is bound to Events at runtime.

Story = Scenario + Events

A Story is a bounded, causal execution object with a beginning, middle, and end.

6. The System Stories Execution Model

Scenario-driven systems introduce a new set of first-class execution objects:

  • Storyboards organize Workflows
  • Workflows contain Scenarios
  • Scenarios bind Events at runtime to produce Stories

This creates a simple hierarchy:

text
Storyboard
 └── Workflow
      └── Scenario (template)
           + Events (runtime)
           = Story (instance)

The critical distinction:

Engineers never write Stories. They write Scenarios. Stories only exist when the system runs.

This inverts the traditional model:

Instead of:

text
Execute → collect data → reconstruct meaning

We get:

text
Declare Scenario → execute → Story is instantiated

7. Why This Is Not Prompting

A prompt tells a model or agent what to do inside a run.

A Scenario defines:

  • What the run is
  • What phases exist
  • What variables matter
  • What success and failure mean

Prompts are instructions.

Scenarios are executable structure.

This is not prompt engineering.

This is scenario engineering.

8. From Observability to Sensemaking

This does not replace:

  • Logs
  • Metrics
  • Traces

It sits above them.

  • Observability shows signals
  • Scenarios define meaning
  • Stories provide understanding

We do not summarize data into stories.

We structure execution so the story already exists.

9. Why This Matters More in the Age of Agents

Agent systems:

  • Are non-deterministic
  • Run long plans
  • Call many tools
  • Fail in surprising ways

Without scenario-bounded execution:

  • You cannot tell what a "run" even is
  • You cannot compare outcomes
  • You cannot supervise behavior

Scenarios provide:

  • Explicit intent
  • Explicit structure
  • Explicit success and failure conditions

Stories provide:

  • Bounded execution
  • Causal traceability
  • Human and machine alignment

10. A New Control Surface for Software

This is not a UI change.

It is a new execution paradigm:

  • Intent-first
  • Structure-first
  • Story-native

It turns:

  • Debugging into inspection
  • Incidents into stories
  • Supervision into a first-class activity

11. Strategic Implications

We are moving from:

Systems that produce data

To:

Systems that produce understandable behavior

And from:

Agents that act

To:

Agents that can be supervised, inspected, and trusted

12. Conclusion

The future interface is not:

  • More dashboards
  • More charts
  • More telemetry

It is:

Intent + Structure + Time

In other words:

Scenarios that become Stories.

One-Sentence Summary

We don't turn data into stories. We turn scenarios into data.