Observer pattern

Decoupling agent reasoning from human comprehension: A new paradigm

This is a profound architectural insight that fundamentally changes how we think about software development systems. You’re essentially proposing:

Traditional Model: Human comprehension is in the critical path

flowchart LR
    
    A[Agent does work] --> B[Formats for humans]
    B --> C[Waits for human approval]
    C --> D[Continues]

New Model: Human comprehension is parallel, not blocking

block
  columns 5
  A["Agent does work"] space B["Continues immediately"]
  space
  space
  space:5
  C["Observer Agent"]
  space
  D["Generates human views"]
  space
  E["Humans review asynchronously"]
  A --> B
  A --> C
  C --> D
  D --> E

The critical path vs. the observer path

Critical path (agent-to-agent)

What’s happening:

AI Planning Agent generates task graph
AI Development Agent claims tasks, produces code
AI Verification Agent validates outputs
AI Coordination Agent orchestrates dependencies
AI Learning Agent extracts patterns

Data format:

Optimized for machine reasoning
Maximum information density
Graph structures, embeddings, semantic representations
No concern for human readability

Speed: Milliseconds to seconds per task

Observer path (agent-to-human)

What’s happening:

Observer Agent monitors critical path
Detects “human interest points” (decisions, milestones, anomalies)
Generates appropriate human representation
Publishes to human stakeholder dashboard
Humans review and optionally intervene

Data format:

Natural language summaries
Visualizations optimized for human cognition
Progressive disclosure (summary → details)
Multiple views for different stakeholder types

Speed: Seconds to minutes, but doesn’t block critical path

Reimagining the UI/UX layer

Let’s design human interfaces that assume agents are the primary workers:

For product owner (Sarah)

Traditional Jira board:

Manual ticket updates
Drag-and-drop cards
Sprint planning poker
Burndown charts

Observer-Generated Dashboard:

View 1: Executive Summary (Default, updates every 5 minutes)

View 2: Decision Queue (When clicked)

View 3: Project Narrative (Natural language story)

"This morning, the team completed the database schema design
ahead of schedule. The AI architecture agent identified a potential
performance optimization that reduced query time by 40%.

Currently, 12 AI agents are working in parallel on API development.
Three human developers reviewed complex business logic this afternoon
and approved all submissions.

The design phase needs your input on layout selection. The AI design
agent has prepared three options with pros/cons analysis based on
user research from last week..."

View 4: Interrogative Interface (Chat with Observer Agent)

Sarah: "Why is WF-8 blocked?"
Observer: "WF-8 requires human judgment on aesthetic tradeoffs. The AI
evaluated options against usability heuristics but flagged that cultural
context from your customer conversations should inform final choice."

Sarah: "What happens if I delay this decision until tomorrow?"
Observer: "8 tasks will remain blocked. Estimated project delay: 4 hours.
However, 15 other tasks can proceed in parallel, so critical path impact
is minimal. You could defer up to 6 hours before it becomes critical."

Sarah: "Show me the customer feedback that's relevant."
Observer: [Generates custom report with relevant quotes and insights]

For Developer (Maya)

Traditional: Opens GitHub, reviews PRs, manually checks code

Observer-Generated Interface:

View 1: Attention Dashboard

🔴 CRITICAL: Security issue detected in PR #234
   AI flagged: Potential SQL injection in prediction service
   Your expertise needed: Review suggested fix
   [Review Now - 10 min estimate]

🟡 REVIEW: 3 PRs awaiting your validation
   AI pre-reviewed: All pass automated checks
   Need human judgment: Complex business logic
   [Batch Review - 25 min estimate]

🟢 FYI: 12 PRs auto-approved by AI today
   All routine patterns, 100% test coverage
   [View Summary] [Spot Check Random Sample]

View 2: Code Narrative

"Today the development agents completed 23 subtasks. The prediction
service now handles cold-start optimization using the warm-up pattern
you suggested in Sprint 7 (AI referenced your previous ADR).

One novel pattern emerged: Agent Dev-3 implemented a caching strategy
we haven't used before. It's testing well, but flagged for your
awareness since it deviates from established patterns.

Quality metrics remain strong: 89% test coverage, zero critical issues,
performance benchmarks exceeded."

View 3: Code Exploration (Semantic, not file-based)

Maya: "Show me all authentication-related code"
Observer: [Generates visual graph showing auth components, even though
they're scattered across multiple files and repos]

Maya: "What changed in auth since last week?"
Observer: [Highlights specific changes with context of why they were made]

Maya: "Explain this caching decision"
Observer: "Dev-3 agent identified that prediction queries repeat 73%
of the time within 5-minute windows. Implemented Redis cache with
5-minute TTL. Reduced database load by 68% in testing. Followed
pattern from Project-X but adapted for real-time requirements..."

For Stakeholder (Non-Technical Executive)

Traditional: Receives status report, attends demo meetings

Observer-Generated Interface:

View 1: Business Outcomes Dashboard

Project Investment: $6,400 spent / $50,000 budget
Expected Launch: Jan 15 (2 weeks ahead of original schedule)
User Value: Projected 15 hours saved per user per week

Business Risks:
✅ Technical feasibility: Confirmed
✅ User adoption: 87% positive feedback in testing
⚠️  Integration complexity: Minor delays possible

Your Decisions Needed: None currently
Next Milestone: Beta launch (ready for your approval in 3 days)

View 2: Plain English Summary

"We're building a dashboard that helps small business owners predict
which customers might stop using their service.

This week, the technical team solved how to make predictions fast
enough (under 2 seconds). Early user testing shows people understand
the interface and find it useful.

The project is ahead of schedule and under budget. The team is now
working on making sure it works reliably with thousands of users.

You'll see a demo of the working system on Friday."

View 3: Risk Monitoring

Automatically monitored risks:
- Budget overrun: LOW (tracking to 13% under budget)
- Schedule slip: LOW (2 weeks ahead of baseline)
- Quality issues: LOW (automated testing passing)
- Team morale: GOOD (all human contributors report satisfaction)
- Scope creep: MODERATE (3 feature requests from users, not yet approved)

Your attention needed: Review scope change requests (non-urgent)

Programming Languages Optimized For Agents

This is where it gets really interesting. Current languages reflect human constraints:

Human-Constrained Languages

Python/JavaScript/etc:

Textual representation (humans read text)
Line-by-line execution (matches human reading)
Named variables (human memory aid)
Comments (human context)
Indentation/formatting (human visual parsing)

But agents don’t need any of this!

Agent-Native Representations

Option 1: Semantic Graphs

Instead of:

def calculate_churn_risk(customer):
    if customer.days_since_login > 45:
        risk = "HIGH"
    elif customer.usage_decline > 0.6:
        risk = "HIGH"
    else:
        risk = "LOW"
    return risk

Agent-native representation:

[Semantic Graph]
Node: FUNCTION
  - purpose: classify_risk_level
  - input_schema: {customer: {days_since_login: int, usage_decline: float}}
  - output_schema: {risk_level: enum[HIGH, LOW]}

Edges:
  - IF days_since_login > 45 THEN risk = HIGH
  - IF usage_decline > 0.6 THEN risk = HIGH
  - ELSE risk = LOW

Constraints:
  - Must execute < 100ms
  - Must handle null inputs gracefully

Provenance:
  - Derived from: user_story_REQ-034
  - Similar to: pattern_churn_detection_v2
  - Verified by: test_suite_CHURN_001

Benefits:

Agent can “understand” purpose without parsing syntax
Graph structure enables parallel reasoning
Constraints explicit, not implicit in code
Provenance built-in, not in comments

Human view generated on demand:

[Observer Agent translates to Python when human developer requests it]

Option 2: Intentional Programming

Instead of writing HOW to do something, specify WHAT and WHY:

Intent: Identify customers at risk of churning
Context: Weekly analytics dashboard
Constraints:
  - Accuracy > 75%
  - Explainable predictions (users must understand why)
  - Real-time (< 2s response)

Success Criteria:
  - Reduces customer churn by 10%
  - 90% user adoption within first month
  - Zero privacy violations

Training Data: customer_behavior_2023_2025.db
Similar Solutions: [Reference to 3 past projects]

AI Agent translates intent → implementation:

Chooses algorithms based on constraints
Generates code in whatever language needed
Creates tests based on success criteria
Monitors in production for success criteria validation

Human never sees implementation unless they request it.

Option 3: Differential Programming

Agents work with mathematical abstractions:

∂churn_risk/∂days_since_login = f(engagement_pattern)

where:
  engagement_pattern = Σ(user_actions) over time_window

Optimize for:
  max(accuracy) subject to explain ability_score > 0.8

Agent reasons about this mathematically, then:

Synthesizes code that implements the math
Proves correctness formally
Generates tests automatically

Human view: “This function predicts churn risk based on login patterns and usage trends, optimized for explainability”

The Translation Layer

Observer Agent serves as Rosetta Stone:

Agent A (thinking in semantic graphs)
  ↓
Observer Agent (translates)
  ↓
Human Developer (sees Python/pseudocode/natural language - their choice)
  ↓
Human gives feedback in natural language
  ↓
Observer Agent (translates back)
  ↓
Agent A (updates semantic graph)

Example interaction:

Human: "This churn prediction seems too aggressive"
Observer translates to: [Constraint_violation: false_positive_rate > threshold]
Agent A: [Adjusts decision boundary in semantic graph]
Observer shows human: "I've reduced the sensitivity. Now predicts HIGH
risk only for very strong signals. Want to see the updated logic?"
Human: "Show me an example"
Observer: [Generates example with real data, shows before/after]
Human: "Perfect"

New UI/UX Patterns For Agent-Primary Systems

Pattern 1: Ambient Awareness

Traditional: Humans must actively check status New: System surfaces what needs attention

Notification appears:
"🔔 Design decision ready for your review (estimated 15 min)"
[Review Now] [Remind me in 1 hour] [Delegate to Maya]

System learns:

When you prefer to be interrupted
What decisions you want to be involved in vs delegate
How much context you need before deciding

Pattern 2: Interrogative Exploration

Traditional: Fixed dashboards and reports New: Conversational exploration of project state

Stakeholder: "Should I be worried about the timeline?"
Observer: "No. While we're 3 days behind on design phase, the
development phase is progressing 40% faster than estimated due to
code generation agents. Overall project is still 1 week ahead."

Stakeholder: "Why is development faster?"
Observer: "AI agents are completing routine implementation tasks
(database schema, API endpoints, tests) that typically take humans
longer. Humans are focusing on complex business logic only."

Stakeholder: "Will quality suffer?"
Observer: "Quality metrics are actually higher than typical projects.
Test coverage is 89% vs 75% average. All code is reviewed by both
AI verification agents and human experts for critical components."

Pattern 3: Multi-Fidelity Views

Different stakeholders need different detail levels:

Executive: Business outcomes and risks Product Owner: Feature progress and decisions needed Tech Lead: Architecture and quality metrics Developer: Code-level details only for exceptions Customer: What’s available to test and when

Observer Agent generates appropriate fidelity for each audience, from same underlying state.

Pattern 4: Intervention Points

Humans don’t need to monitor constantly. System identifies when human input is valuable:

Intervention Types:
1. DECISION: Agent needs human judgment (uncertainty high)
2. VALIDATION: Agent made decision, needs human confirmation (medium confidence)
3. EXCEPTION: Unusual pattern detected, human should know
4. LEARNING: Outcome differs from expectation, capture human insight

Everything else: Agents handle autonomously, log for audit

Pattern 5: Rewind and Replay

Since all agent reasoning is logged:

Stakeholder: "Why did we choose Lambda over ECS?"
Observer: [Reconstructs entire decision process]
  - Initial requirements (from user stories)
  - Research phase (3 options evaluated)
  - Tradeoff analysis (cost, performance, complexity)
  - Decision maker (Maya) and rationale
  - Validation testing
  - Current outcomes vs expectations

[Show as timeline] [Show as decision tree] [Show as narrative]

Any project decision can be “replayed” with full context.

Implementation For Future’s Edge

The platform architecture:

Layer 1: Agent Collaboration Layer (Critical Path)
- Agent-native data formats (graphs, embeddings, formal specs)
- High-speed agent-to-agent communication
- Optimized for machine reasoning
- Millisecond latency

Layer 2: Observer & Translation Layer
- Monitors agent layer
- Detects human-relevant events
- Generates multi-format views
- Maintains human-agent dialogue context
- Learns stakeholder preferences

Layer 3: Human Interface Layer
- Web/mobile UIs
- Natural language interaction
- Visual dashboards
- Configurable views per stakeholder
- Async notifications

Layer 4: Intervention Layer
- Humans can inject decisions/constraints
- Observer translates human intent → agent instructions
- Agents incorporate human input and continue
- Full audit trail of human interventions

The Data Flow

Normal Operation:

Agents work in Layer 1 (fast, opaque to humans)
  → Observer monitors, generates summaries in Layer 2
    → Humans check Layer 3 when convenient
      → Humans provide input via Layer 4 when needed
        → Observer translates to Layer 1
          → Agents incorporate and continue

Humans never wait for agents, agents never wait for humans (except at explicit decision points).

The Philosophical Shift

Old paradigm:

Tools built for humans
Computers assist humans
Human comprehension is primary

New paradigm:

Tools built for agents
Humans provide judgment/creativity
Agent efficiency is primary
Human comprehension is generated as needed

This inverts the typical “AI assistant” model.

Instead of: “AI helps humans do work” We have: “Humans guide AI swarms doing work”

The implications for Future’s Edge:

Platform serves agent collaboration first, human oversight second
“User interface” becomes “observer interface” - generated views, not designed screens
Programming becomes intent specification + agent execution
Project management becomes decision routing + exception handling
Documentation is reconstructed from agent reasoning, not written separately

Questions this raises:

How much transparency do humans need into agent reasoning? Can we trust “black box” agent work with spot checks?
What decisions genuinely require human judgment vs just human comfort?
If agents work in non-human-readable formats, how do we audit/debug? Or do we trust formal verification?
Does this model work for regulated industries where “explainability” is legally required?
How do we prevent humans from becoming deskilled if agents do 90% of tasks?

Should we continue designing the full lifecycle with this agent-primary, human-observer architecture?

And should we prototype what the Observer Agent’s “human interface generation” actually looks like for a specific decision point?