The Limitations of Static LLMs
Section 01Passive Nature
Traditional models function as "static solvers." They process input in a single pass without the capacity to influence or change their environment.
Fragility & Cascading
Minor errors compound rapidly. Current models struggle to backtrack or recover, leading to total task failure in complex, long-horizon scenarios.
Probabilistic Hallucinations
Lacking deterministic grounding, LLMs struggle with strict logic and arithmetic, often inventing facts based on patterns rather than truth.
The Three Pillars of Agency
Section 02Foundational Reasoning
- Advanced Planning: Decomposing complex tasks into small, executable steps.
- Program-Aided: Grounding logic in deterministic code (Python) to eliminate hallucinations.
- Enhanced Tool Use: "Act and Verify" cycles with repository-level operations.
Self-Evolving Reasoning
Agents refine processes through experience. Using persistent memory substrates and feedback loops for continuous self-correction and adaptation.
Collective Reasoning
Collaborative intelligence where multiple agents assume roles (Manager, Worker, Verifier) to solve tasks through distributed cognitive labor.
Pathways to Execution
Section 03In-Context Reasoning
FLEXIBLELeverages pre-trained models with sophisticated system prompts and external scripts. Zero training compute, but high operational token costs.
Rapid iteration, flexible
Inefficient, high API cost
Post-Training Reasoning
EFFICIENTIntegrates agentic logic directly into weights via RL or fine-tuning. Higher upfront cost, but native understanding and faster inference.
Small, fast, native skill
High training investment
Applied Agentic Systems
Section 04Scientific Research
Deep Researcher Agents
Agents that browse the live web, form research plans, and corroborate conflicting data to ensure scientific integrity with full provenance.
Software Engineering
Autonomous Coding
Moving beyond "vibe coding" to full repository management: handling dependencies, checking breaking changes, and refactoring autonomously.
Knowledge Engines
Paper QA Systems
Prioritizing factual grounding with citations for every piece of information, creating a verifiable link between thought and data.
The Future of Non-Fungible AI
We are moving towards an era of personalized AI. Agents that curate their own memories and learn procedural skills based on individual work patterns, creating unique instances of tailored expertise.
Maintaining reliability over extended operational periods.
Developing a robust understanding of the environment and its rules.