AGI Prototype Achieves Real-Time Self-Correction: 95% Success on Simple Tasks

This article covers AI developments from December 2025.

AGI Prototype Shows Real-Time Self-Correction

A groundbreaking AGI prototype demonstrates capabilities once thought years away: real-time action planning and self-correction based on visual input, with upcoming features including self-awareness and autonomous task execution. Follow the running story in our AI agents news hub.

Current Capabilities

The prototype already exhibits:

Visual Understanding:

• Process camera/screen input in real-time
• Understand spatial relationships
• Recognize objects and contexts
• Track changes over time

Action Planning:

• Generate step-by-step plans
• Adapt plans to changing conditions
• Optimize for efficiency
• Handle multi-step tasks

Self-Correction:

• Detect when actions fail
• Analyze failure causes
• Generate alternative approaches
• Retry with improved strategy

Real-World Demonstration

Example Task: "Make Coffee"

1. Agent observes kitchen via camera
2. Plans: Get cup → Add coffee → Add water → Start machine
3. Executes: Reaches for cup
4. Observes: Cup knocked over
5. Self-corrects: "Need to approach differently"
6. Replans: Stabilize cup first, then proceed
7. Successfully completes task

The Self-Correction Loop

How It Works:

Observe → Plan → Act → Evaluate
   ↑                      ↓
   └──── Adjust ←────────┘

Key Components:

1. Observation: Visual input processing
2. Planning: Action sequence generation
3. Execution: Physical or digital actions
4. Evaluation: Success/failure detection
5. Adjustment: Strategy modification

Upcoming Features

Self-Awareness:

• Understanding own capabilities and limitations
• Recognizing when to ask for help
• Tracking performance over time
• Metacognitive reasoning

Autonomous Tasks:

• Unsupervised goal achievement
• Long-running background processes
• Multi-day projects
• Proactive problem-solving

Built with Unrestricted LLMs

Why This Matters:

The prototype uses unrestricted LLMs rather than fine-tuned, constrained models:

Advantages:

• Full reasoning capabilities
• Flexible problem-solving
• Natural language understanding
• General knowledge access
• Creative solutions

True Agentic Behavior:

• Not following predetermined scripts
• Genuine reasoning about problems
• Adaptive to novel situations
• Learning from experience

Technical Architecture

Input Layer:

• Visual: Camera/screen capture
• Context: Environment state
• Goals: Task specifications
• Memory: Past experiences

Processing Layer:

• Vision model: Scene understanding
• Language model: Reasoning and planning
• Action model: Movement/interaction
• Evaluation model: Success assessment

Output Layer:

• Physical actions: Robot control
• Digital actions: Software interaction
• Communication: Status updates
• Learning: Strategy refinement

Performance Metrics

Success Rate by Task Complexity:

• Simple tasks (1-3 steps): 95%
• Medium tasks (4-10 steps): 78%
• Complex tasks (10+ steps): 62%
• Novel tasks (never seen): 45%

Self-Correction Rate:

• Detects failures: 89%
• Generates alternatives: 76%
• Successfully recovers: 68%

Applications in Development

Physical World:

• Household robots
• Manufacturing automation
• Warehouse operations
• Medical assistance

Digital World:

• Software development
• Data analysis
• Research tasks
• Customer service

Hybrid:

• Laboratory experiments
• Quality control
• Training simulations
• Human-robot collaboration

Comparison to Existing Systems

Traditional Agents:

• Fixed behavior scripts
• Limited adaptation
• No self-correction
• Narrow domains

This Prototype:

• Dynamic planning
• Real-time adaptation
• Self-correction loops
• General capabilities

Challenges Being Addressed

Current Limitations:

1. Speed: Planning can be slow for complex tasks
2. Reliability: Not yet production-ready
3. Safety: Ensuring safe self-correction
4. Generalization: Transfer to new domains
5. Efficiency: Computational requirements

Active Research:

• Faster inference methods
• Robust failure recovery
• Safety constraints during self-correction
• Few-shot learning for new tasks
• Model compression

Timeline to Production

Phase 1 (2025): Controlled environments, supervised operation Phase 2 (2026): Semi-autonomous in structured settings Phase 3 (2027): Fully autonomous for defined task sets Phase 4 (2028+): General-purpose AGI agents

Ethical Considerations

Questions Raised:

• How much autonomy should agents have?
• Who's responsible for self-corrected actions?
• When should agents ask for human approval?
• How to ensure alignment during self-improvement?

Safety Measures:

• Human override capabilities
• Action bounds and constraints
• Logging and auditability
• Staged rollout with monitoring

The Path to True AGI

These results connect directly to the 3 pillars of AGI—and Google's parallel work on Titans + MIRAS memory. This prototype demonstrates that key AGI capabilities are achievable now:

✅ Real-time perception ✅ Dynamic planning ✅ Self-correction 🔄 Self-awareness (in development) 🔄 Autonomous operation (in development) ❓ Consciousness (philosophical question)

We're closer than most realize.

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Related: 3 Pillars of AGI: Agency, Alignment & Memory · Google Titans + MIRAS Memory System · AI Timelines Compressing Toward AGI · Stanford AI Index 2026 · AI Agents News