Designing LLM Applications with Partial Autonomy: Building “Iron Man Suits” for Human Intelligence

How to move beyond simple chat interfaces to create powerful, controllable AI tools that augment human capability

Introduction: Beyond the Chat Interface

The current generation of LLM applications often feels like talking to a very smart but unreliable assistant through a text box. You ask a question, get an answer, and hope it’s correct. But what if we could build something more powerful—something that acts like an “Iron Man suit” for your brain, augmenting your capabilities while keeping you firmly in control?

This is the promise of partial autonomy in LLM applications: systems that can handle complex, multi-step tasks autonomously, but always maintain a fast generation-verification loop with human oversight. Instead of replacing humans, these systems amplify human intelligence by managing the cognitive deficits of LLMs—hallucinations, fixed memory, and context limitations—through thoughtful architectural design.

Generation-Verification Loop The generation-verification loop: Speed is key. If humans can’t quickly verify AI output, they become the bottleneck.

Fundamental Questions in Partial Autonomy Design

Before diving into solutions, we must address four critical questions that shape how we design these systems:

Fundamental Questions Four fundamental questions that guide partial autonomy design.

  1. “What does all software look like in the partial autonomy world?”
    As AI becomes more capable, every application will need to consider how humans and AI collaborate. The question isn’t whether AI will be integrated, but how.

  2. “Can an LLM ‘see’ all the things the human can?”
    LLMs have different perceptual capabilities than humans. They can process vast amounts of text and code instantly, but they can’t visually inspect a running application or understand non-verbal cues. Design must account for these differences.

  3. “Can an LLM ‘act’ in all the ways a human can?”
    LLMs can generate code, make API calls, and manipulate files, but they can’t physically interact with systems or make judgment calls in ambiguous situations. Understanding these boundaries is crucial for safe autonomy.

  4. “How can a human supervise and stay in the loop?”
    This is the core challenge: creating interfaces and workflows that make human oversight fast, effective, and non-fatiguing.

In this deep dive, we’ll explore:

The Problem: Why Simple Chat Interfaces Fall Short

Before diving into solutions, let’s understand the fundamental limitations of LLMs that make simple chat interfaces inadequate for complex tasks:

1. Hallucinations

LLMs can confidently produce incorrect information. Without verification mechanisms, users can’t distinguish between accurate and fabricated outputs.

2. Fixed Memory & Context Drift

LLMs have limited context windows. In a chat interface, users must manually copy-paste relevant information, leading to context loss and inefficiency.

3. Lack of Specialization

A single model trying to do everything (reasoning, retrieval, code generation) performs worse than specialized models orchestrated together.

4. Auditability Challenges

Text-based outputs are difficult to verify. How do you quickly check if an AI-generated code change is correct? Or if a research answer cites real sources?

5. All-or-Nothing Autonomy

Most systems either require constant human input or operate completely autonomously. There’s no middle ground for different risk levels.

The Solution: Core Architectural Pillars

Effective partially autonomous LLM applications share several key architectural features. Let’s break them down:

1. Context Management: Feeding the Model’s “Working Memory”

The Problem: Users shouldn’t have to manually copy-paste data into a chat window. The application should automatically manage what information the LLM sees.

The Solution: Implement intelligent context retrieval systems that:

Example: When you ask Cursor to modify a function, it automatically loads the file, related imports, and relevant documentation—not just what you’ve manually pasted.

2. Orchestration of Multiple Specialized Models

The Problem: Using one model for everything leads to suboptimal performance.

The Solution: Orchestrate multiple specialized models:

Example: Perplexity uses separate models for search query generation, web retrieval, fact extraction, and synthesis—each optimized for its specific task.

3. Application-Specific GUI: The “Highway to the Brain”

The Problem: Text-based interfaces make verification slow and error-prone. Humans process visual information much faster than text.

The Solution: Build dedicated GUIs that leverage human visual processing:

Why It Matters: A developer can verify a code diff in seconds by scanning red/green highlights, but would take minutes reading through text descriptions of changes.

4. The Autonomy Slider: Configurable Delegation

The Problem: Different tasks require different levels of AI autonomy. High-risk tasks need more oversight; low-risk tasks can be more autonomous.

The Solution: Implement an autonomy slider—a UI control that lets users tune how much control they delegate to the AI:

Low Autonomy ←──────────────→ High Autonomy
(Safer, Slower)              (Faster, Riskier)

Key Principle: The autonomy level should map to the scope and risk of the task:

Autonomy Slider The autonomy slider: Four levels of delegation from low-risk tab completion to high-risk repository-wide changes.

Case Study 1: Cursor - The Code Editor with Partial Autonomy

Cursor exemplifies partial autonomy in software development. Let’s examine how it implements each architectural pillar:

The Autonomy Slider: Four Distinct Levels

Cursor provides a granular autonomy slider with four distinct levels:

Level 1: Tab Completion

Level 2: Command K (Selection-Based Editing)

Level 3: Command L (File-Level Editing)

Level 4: Command I (Repository-Wide Agent Mode)

Why This Design Works

Prevents “10,000-Line Diffs”: By keeping the AI “on a leash” with scoped autonomy levels, Cursor prevents the AI from generating massive, unverifiable changes. Users can incrementally increase autonomy as they build trust.

Cursor vs Perplexity Comparison Side-by-side comparison: How Cursor and Perplexity implement partial autonomy differently for coding vs. research.

Fast Verification Loop: The visual diff interface allows developers to verify changes in seconds using pattern recognition rather than reading every line.

Context Management: Cursor automatically manages context by:

Model Orchestration: Under the hood, Cursor uses:

Lessons Learned

  1. Start Small: Users begin with tab completion and gradually increase autonomy as they gain confidence.
  2. Visual Verification is Key: The red/green diff view is the “highway to the brain” that makes verification effortless.
  3. Scope Control: Limiting the scope of changes prevents catastrophic errors and makes rollback easier.

Case Study 2: Perplexity - Research with Partial Autonomy

Perplexity applies partial autonomy principles to information retrieval and research. Let’s see how:

The Autonomy Slider: Search → Research → Deep Research

Perplexity offers three main autonomy levels:

Research Mode

Deep Research Mode

Architectural Implementation

Auditability via GUI:

Orchestration:

Context Management:

Autonomy Levels: Perplexity’s autonomy slider maps to different research depths:

Why This Design Works

Transparency: Users can verify every claim by clicking through to sources. This addresses the hallucination problem directly.

Incremental Depth: Users can start with quick search, escalate to research for more depth, and use deep research for comprehensive investigations when needed.

Export & Share: Research results can be exported as PDFs or shared pages, making them useful artifacts, not just ephemeral chat responses.

Limitations & Mitigations

Hallucinations Still Occur: Even with citations, Perplexity can fabricate sources or misattribute quotes. The GUI makes it easier to catch these errors, but users must still verify.

Time Trade-off: Deep research takes minutes, not seconds. This is intentional—more thorough research requires more time.

System Design: Architecture of a Partially Autonomous LLM Application

Let’s break down the system architecture into concrete components:

High-Level Architecture

┌─────────────────────────────────────────────────────────────┐
│                    User Interface Layer                      │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │ Autonomy     │  │ Visual Diff  │  │ Source       │      │
│  │ Slider       │  │ Viewer       │  │ Citations    │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│              Preprocessing & Task Decomposition              │
│  • Break task into subtasks                                  │
│  • Identify autonomous vs. supervised components             │
│  • Determine required context                                │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│              Context Management & Retrieval                  │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │ Embedding    │  │ Vector DB    │  │ File         │      │
│  │ Model        │  │ / Index      │  │ System       │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│              Model Orchestration Layer                       │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │ Reasoning    │  │ Code Gen     │  │ Verification │      │
│  │ Model        │  │ Model        │  │ Model        │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
│  ┌──────────────┐  ┌──────────────┐                        │
│  │ Embedding    │  │ Diff/Apply   │                        │
│  │ Model        │  │ Model        │                        │
│  └──────────────┘  └──────────────┘                        │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│              Autonomy Controller                             │
│  • Policy engine (what's allowed per autonomy level)        │
│  • Checkpoint management                                    │
│  • Rollback mechanisms                                      │
│  • User confirmation gates                                  │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│              Output Generation & Formatting                  │
│  • Generate diffs/changes                                   │
│  • Format citations                                         │
│  • Create visual representations                            │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│              Feedback Loop                                   │
│  • Human verification                                       │
│  • Approval/rejection/modification                          │
│  • Model refinement based on feedback                       │
│  • Learning from corrections                                │
└─────────────────────────────────────────────────────────────┘

System Architecture Complete system architecture: From user interface to feedback loop, showing how all components work together.

Component Details

1. User Interface Layer

2. Preprocessing & Task Decomposition

3. Context Management & Retrieval

4. Model Orchestration Layer

5. Autonomy Controller

6. Feedback Loop

Design Principles: Building Your Own Partial Autonomy System

Based on the case studies and architectural analysis, here are key principles to follow:

1. Speed Up Verification: The “Highway to the Brain”

Principle: If humans can’t quickly verify AI output, they become the bottleneck. The GUI is the “highway to the brain”—it’s how information flows efficiently from AI to human understanding.

The Generation-Verification Loop: The core workflow is a continuous loop:

  1. AI Generation → Context retrieval, model orchestration, output creation
  2. Visual Output → Diffs, citations, side-by-side comparisons
  3. Human Verification → Pattern recognition, approve/reject/modify
  4. Feedback → Model refinement, pattern learning
  5. Loop Continues → Back to generation with improved context

Detailed Generation-Verification Loop Detailed view of the generation-verification loop with key principles: Make verification easy and fast (GUI is highway to brain), and keep AI on a tight leash.

Key Insight: Keep AI “on a tight leash” to increase the probability of successful verification. By limiting scope and autonomy, you reduce the cognitive load on humans and make verification faster.

Implementation:

Example: A code diff with red/green highlighting can be verified in seconds using visual pattern recognition. A text description of changes would take minutes of careful reading.

2. Work in Small, Concrete Chunks

Principle: Vague prompts lead to high failure rates. Small, well-scoped tasks are easier to verify and correct.

Implementation:

Example: Instead of “refactor the entire codebase,” use “refactor this function to use async/await” → “refactor this file” → “refactor this module.”

3. Agent-Legible Infrastructure

Principle: Prepare your software and documentation for AI consumption.

Implementation:

Example: Instead of “Go to Settings → Preferences → Editor,” provide: curl -X PUT /api/settings/editor/preferences

4. The lm.txt Standard (Emerging Practice)

Principle: Similar to robots.txt, provide a standard way to tell LLMs about your domain. Note: This is an emerging idea, not yet a widely adopted standard, but represents an important direction for agent-legible infrastructure.

Current Status: The lm.txt concept (also sometimes called llms.txt) is being discussed in the community but has not been officially adopted by major LLM providers. However, the principle of making domains more legible to AI agents is gaining traction, especially with protocols like MCP (Model Context Protocol).

Implementation:

Example lm.txt:

# My Application Domain

## Purpose
This is a financial trading application. LLMs should not execute trades autonomously.

## Allowed Actions
- Read market data
- Generate analysis reports
- Suggest trading strategies (with human approval)

## Prohibited Actions
- Execute trades
- Modify account settings
- Access user credentials

## Key Concepts
- Portfolio: Collection of investments
- Position: Individual holding in a security
- Risk: Measured in standard deviations

## Available Tools/APIs
- GET /api/market/data/{symbol} - Retrieve market data
- POST /api/analysis/generate - Generate analysis (requires approval)

Related: The Model Context Protocol (MCP) introduced by Anthropic in late 2024 provides a standardized way for applications to provide context to LLMs, enabling dynamic tool discovery and selection—a crucial enabler for partial autonomy systems.

5. Transparent Source Handling

Principle: Always show where information comes from, and make it easy to verify.

Implementation:

6. Flexible Autonomy Levels

Principle: Different tasks require different levels of autonomy. Make it configurable.

Implementation:

Design Principles Checklist Visual checklist of key design principles for building effective partial autonomy systems.

Handling Failures: When Things Go Wrong

Even well-designed systems fail. Here’s how to handle common failure modes:

Hallucinations

Problem: LLMs generate plausible-sounding but incorrect information.

Mitigation:

Scope Creep

Problem: AI makes changes beyond the intended scope.

Mitigation:

Context Loss

Problem: LLM loses track of conversation or project context. This is especially problematic across sessions—agents forgetting project state, requiring repeated explanations, or losing track of earlier decisions.

Mitigation:

Real-World Impact: Users report frustration when Cursor forgets earlier decisions or requires re-explaining project structure. This highlights the importance of persistent, well-managed context.

Verification Fatigue

Problem: Users get tired of verifying every small change.

Mitigation:

Cost and Complexity Trade-offs

Problem: While tools like Cursor speed up development velocity in the short term, they can lead to increased code complexity and technical debt over time.

Research Finding: Studies show that Cursor adoption leads to faster development but also persistent increases in code complexity and static analysis warnings. The speed gain comes with a quality cost.

Mitigation:

DevOps and Deployment Challenges

Problem: “You can code in 1 day but it takes 7 days to deploy.” Partial autonomy systems can generate code quickly, but deployment, testing, and integration still require human oversight and time.

Mitigation:

Future Directions

The field of partial autonomy is rapidly evolving. Here are emerging trends:

1. Temporal Memory and Context Persistence

Systems that remember past interactions and learn from them, reducing the need for repeated context. Tools like MemTool demonstrate adaptive memory management that balances autonomy with deterministic control.

2. Better Context Management

More sophisticated retrieval systems that understand relationships between pieces of information. Context pruning policies that intelligently manage what stays in memory vs. what gets archived.

3. Standardization: MCP and Beyond

The Model Context Protocol (MCP) is gaining adoption as a standard for how applications provide context to LLMs. This enables dynamic tool discovery and reduces brittleness in agent systems. The lm.txt concept may evolve into a more formal standard.

4. Hybrid Models and Specialized Orchestration

Combining specialized models more seamlessly, with automatic model selection based on task. Prompt routing systems that send queries to specialized modules (technical support, summarization, code patches) improve accuracy.

5. Improved Safety Mechanisms

Better verification models, automated testing of AI outputs, and more robust rollback systems. Frameworks like AgentSpec provide DSLs for enforcing runtime safety constraints, dramatically reducing unsafe behavior.

6. Domain-Specific Autonomy Sliders

Custom autonomy levels tailored to specific domains (e.g., medical diagnosis vs. code refactoring). Different risk profiles require different autonomy configurations.

7. Experience-Based Learning

Systems where agents store “lessons” from past trajectories and refine policies or heuristics through closed-loop feedback, improving autonomy over time.

8. Cost and Latency Optimization

Techniques like paged attention for KV cache management, dynamic batching, and quantization help scale partial autonomy systems while managing costs.

Conclusion: Key Takeaways

Designing LLM applications with partial autonomy requires a shift in mindset:

  1. Think Beyond Chat: Move from simple Q&A interfaces to dedicated tools that augment human capability.

  2. Manage Cognitive Deficits: Use architecture (context management, model orchestration, GUIs) to compensate for LLM weaknesses.

  3. Keep Humans in Control: The autonomy slider isn’t just a feature—it’s a fundamental design pattern for human-AI collaboration.

  4. Speed Up Verification: If verification is slow, humans become the bottleneck. Use visual interfaces and small chunks.

  5. Build for AI Consumption: Structure your systems and documentation so AI agents can understand and interact with them.

  6. Start Small, Scale Up: Begin with low autonomy and let users increase it as they build trust.

The future of LLM applications isn’t full autonomy—it’s intelligent augmentation with human oversight. By designing systems that respect both AI capabilities and human judgment, we can create tools that truly amplify human intelligence.

Resources & Further Reading

Tools & Platforms

Research Papers

Standards & Protocols

This blog post is based on research and analysis of current LLM application design patterns. For the latest updates and discussions, follow me on LinkedIn.