OES Framework: A Docker-Inspired Approach to AI Workflow Management

TL;DR

OES Framework structures AI tasks using three core elements: Objective (clear goals), Environment (containerized context), and Success Criteria (measurable outcomes). Like Docker solved "works on my machine" problems, OES solves "works in my session" problems for AI workflows.

The AI Workflow Dilemma

Even the most advanced AI systems frequently exhibit problematic behaviors:

Task drift: Gradually deviating from original goals as conversations progress
Context amnesia: Forgetting critical information between sessions
Hidden assumptions: Making incorrect inferences from fragmented information

Why This Happens

Unlike humans, AI has strict context window limitations:

Cannot process information beyond its context window
Lacks distinction between working memory and long-term memory
"Forgets" everything when sessions are interrupted or switched

The Fragmentation Problem

In real work scenarios:

We can't provide complete background information at once
AI must work with fragmented data
This forces AI to make hidden assumptions
These assumptions often diverge from our actual expectations

OES Framework: Containerized AI Workflows

Inspired by Docker's containerization approach, OES encapsulates AI tasks into self-contained units:

The Three Core Elements

Element	Purpose	Analogy
Objective (O)	Define specific expected outcomes	Docker's ENTRYPOINT
Environment (E)	Containerize all necessary context	Docker's image layers
Success Criteria (S)	Establish objective acceptance conditions	Docker's health checks

Objective (O): The Directional Compass

A structured objective:

Prevents "task drift" during execution
Reduces incorrect hidden assumptions
Provides decision priority framework
Enables AI self-assessment of progress

What Makes a Good Objective

❌ Vague: "Write some marketing content"
✅ Structured: "Create 3 LinkedIn posts promoting our OES Framework article,
   targeting engineering managers, each under 300 characters"

Environment (E): The Work Container

Environment is the most innovative element, containing four layers:

Layer	Contents	Examples
Information Resources	Task-relevant knowledge and data	API docs, style guides, examples
Constraint Conditions	Technical, business, resource limits	Word count, tone, platform rules
Execution Standards	Style guides, quality benchmarks	Brand voice, formatting rules
Context Relationships	Previous task outputs, overall map	Prior decisions, project scope

Benefits of Environment Containerization

Task Atomization: Each task is self-contained
Execution Consistency: Same environment = same results
Reduced Communication Cost: Less back-and-forth clarification
Efficient Task Handoff: Easy to transfer between sessions/agents

Success Criteria (S): Preventing Half-Measures

Success Criteria ensure AI doesn't deliver incomplete work:

Category	Examples
Result Acceptance	Feature completeness, performance metrics
Completeness Checklist	All required components covered
Quality Framework	Maintainability, extensibility standards
Verification Methods	How to objectively validate success

OES Task Networks

Vertical Connection: Hierarchical Decomposition

Parent Task Success (S) → Child Task Objective (O)

Parent success criteria become child task objectives.

Horizontal Connection: Sequential Dependencies

Sibling Task A (Output) → Sibling Task B Environment (E)

Earlier task outputs become later task environment inputs.

Practical Template

Task: [Brief description]

Objective (O):
- [Specific expected result]
- [Boundaries and constraints]

Environment (E):
- Background: [Relevant context]
- Resources: [Available data, tools, references]
- Constraints: [Technical, business, resource limits]

Success Criteria (S):
- Baseline: [Minimum requirements]
- Expected Quality: [Project quality standards]
- Verification: [How to validate completion]

Real-World Example

Before OES

"Help me write a blog post about our new feature"

After OES

Task: Write announcement blog post for OES Framework launch

Objective:
- Create 800-1200 word blog post announcing OES Framework
- Target audience: Engineering managers and AI practitioners
- Achieve thought leadership positioning for Deepractice

Environment:
- Background: OES Framework solves AI task drift and context loss
- Resources: Framework documentation, use case examples
- Constraints: Publish deadline Friday, SEO-optimized

Success Criteria:
- Baseline: Explains O, E, S components clearly
- Quality: Includes practical code example, engaging headline
- Verification: Reviewed by engineering team, Grammarly score >90

Key Benefits

Before OES	After OES
Task drift mid-conversation	Clear objective prevents deviation
Repeated context explanation	Environment encapsulates all context
Unclear completion criteria	Success criteria define "done"
Inconsistent output quality	Standardized quality benchmarks

Conclusion

Just as Docker solved "works on my machine" by containerizing applications, OES Framework solves "works in my session" by containerizing AI tasks.

Through objective clarification, environment containerization, and success criteria specification, we can build more efficient, reliable AI workflows—turning AI from an unpredictable assistant into a dependable collaborator.

About the Author

Deepractice - Making AI at Your Fingertips