Agent V2 Architecture
Agent V2 is the next major evolution of the Amigo platform. The driving goal is to close the bandwidth gap between memory, knowledge and reasoning so the agent can tackle complex, long-horizon problems with far less external scaffolding.
Building on our partnership model where domain experts build the world/problem models and judges that drive evolutionary pressure, while Amigo focuses on building the efficient, recursively improving system that evolves under that pressure, Agent V2 strengthens this division of responsibilities with architectural improvements that enhance system evolution.
The following subsections give a high-level overview of the three key integration points under development.
Memory-Knowledge-Reasoning Integration
In critical enterprise environments, the ability to handle complex long-range reasoning—beyond solving isolated problems to managing full end-to-end projects—depends on the bandwidth between memory/knowledge systems and reasoning processes. Traditional approaches suffer from weak abstract control and poor integration between these layers.
Amigo's upcoming architecture addresses this bottleneck by enabling an agent to:
Dynamic abstraction control – seamlessly move between different granularity levels depending on reasoning needs.
Contextual reframing – transform stored information into the optimal configuration for the current task.
Quantized problem decomposition – break complex challenges into solvable units that compound toward larger goals.
Inner ↔ Outer World Flow
Outer World (Sensory System) – raw data streams undergo prioritized preprocessing.
Outer–Inner Bridge – information is normalized and compressed (10×–1000×) through intelligent contextual processing.
Inner World Construction – an enriched, quantized representation with well-defined contextual boundaries is built.
Inner World Execution – complex reasoning proceeds as a series of well-defined optimization problems.
Side-effect Translation – internal representations are transformed into interpretable external outputs.
Optimization Problem Approach
Each reasoning step is reframed as an optimization problem:
Problem quantization – each "quantum" becomes a bounded optimization challenge.
Contextual boundaries – problems are formulated with explicit constraints and context.
Dependency chains – larger problems decompose into smaller ones with explicit relationships.
Checkpointing – side-effects provide irreversible progress markers.
This approach lets the agent navigate long reasoning chains while maintaining coherent progress toward long-range goals.
Memory System's Supporting Role
The layered memory architecture supplies exactly the right information density for each reasoning task:
Strategic reasoning – L2 (user model) provides high-level dimensional context.
Tactical decisions – L1 (observations) contributes contextual patterns.
Critical analysis – L0 (ground truth) delivers complete verbatim information when needed.
Knowledge-Reasoning Integration
Knowledge activation is only useful if it shapes the agent's reasoning process. Agent V2 tightens this coupling so that knowledge drives problem-space transformation, not mere information retrieval.
From the knowledge perspective, this integration will:
Optimize latent-space priming so activation happens at the ideal granularity.
Create solvable problem topologies via strategic knowledge representation.
Support quantum problem decomposition by structuring knowledge to make complex challenges tractable.
Through the same optimization-problem lens used in memory integration, knowledge activation will directly influence how the agent perceives, structures and solves problems.
Memory-Reasoning Bridge
The layered memory system also plays a direct role in bridging memory and reasoning:
L2 (User Model) – dimensional context for strategic planning and problem decomposition.
L1 (Observations) – mid-level patterns critical for tactical decisions.
L0 (Ground Truth) – full-fidelity information required for precise reasoning.
By delivering the correct information density at each step, the bridge helps overcome the current token-bottleneck constraints and enables complex, multi-step reasoning that would otherwise be impossible. As Agent V2 matures, this mechanism will underpin long-horizon workflows that exceed today's context-window limits.
Information Loss & Bandwidth
Recent interpretability work (Anthropic 2025 "Attribution Graphs & AI Biology") demonstrates that heavy token compression can produce explanations that are linguistically coherent yet mechanistically unfaithful. Philosopher Harry Frankfurt categorizes this as bullshit—content optimized for plausibility over truth.
What the "token bottleneck" really implies Each forward pass lights up thousands of floating-point activations inside the residual stream—an extraordinarily rich internal thought. Before the model can communicate that thought it must squash the entire pattern into a single probability distribution over ~50 000 discrete tokens. One token is sampled, emitted, and the internal state is flushed: the model ingests its own output and must rebuild context from scratch. This is akin to a human author who may write one character, suffers total amnesia, re-reads the draft, write the next character, and so on. Dropped threads, hallucinated details and the occasional leap into pure nonsense become mathematically inevitable—the exact "bullshit" failure mode Frankfurt warned about.
Why the Failure Occurs
Severe compression – thousands of internal floats → a few UTF-8 bytes.
Heuristic reconstruction – later tokens must rebuild missing detail from language priors.
V1 Counter-Measures
Parallel pre-processing (dynamic-behavior ranking + memory sufficiency) ensures the conscious state machine starts with high-value context.
Behavior-driven problem-space terraforming prunes irrelevant branches before any text emission.
Memory-first recall preserves critical evidence that would otherwise be lost.
V2 Path Forward
Outer World Sensory Queue – priority-filtered ingestion with time-decay.
Outer–Inner Bridge – task-aware smart compression (goal 10×–1000×) into fixed context pools.
Quantized Inner World – continuous streams reframed into optimization quanta; low-value detail never hits the token boundary.
Side-Effect Translation Layer – enables high-bandwidth A2A vector payloads, paving the way for neuralese.
Key takeaway: widening the Memory + Knowledge ↔ Reasoning bridge—first through external scaffolding, later through neuralese—directly lowers the incidence of unfaithful reasoning.
Further reading: Frankfurt (1986) On Bullshit; microscope evidence in Anthropic 2025 paper.
Bandwidth Pipeline (Outer → Inner World)
Outer World
Parallel raw data (text, audio, video, robotics) with light decay & basic signal prioritization.
Outer–Inner Bridge
10-1000× smart compression via contextual normalization and attention-based decay.
Inner World Construction
Builds an enriched, quantized problem space; decomposes big goals into quanta with explicit dependencies.
Inner World Execution
Solves each quantum while side-effects checkpoint irreversible progress.
Side-effect Translation
Converts sparse internal actions into external outputs and feeds them downstream (incl. other agents).
Strategic Evolutionary Direction
Agent V2 architecture development directly supports Amigo's core strategic objectives while strengthening our reliability-first approach. Following our methodology of mastering well-known domains before expanding scope, these interconnected goals will drive our technological evolution:
1. Advanced Problem Space Simulation
Building sophisticated problem space simulators and judges is foundational to our approach. These systems:
Create precise evolutionary pressures that drive agent improvement
Provide objective measurement frameworks for capabilities
Capture domain expertise in executable form
Enable rapid adaptation to changing market conditions
Our development of copilots specifically designed to accelerate the evolution of problem definitions based on research insights will dramatically reduce the cycle time from domain understanding to effective simulation environments.
2. Accelerated Agent Evolution
The Agent V2 architecture is designed to enable rapid, efficient agent adaptation under the evolutionary pressure of simulators and judges. By:
Optimizing the memory-knowledge-reasoning bandwidth
Formalizing reasoning as quantized optimization problems
Creating cleaner side-effect boundaries for measurable progress
We establish the foundation for increasingly recursive improvement, where agents play a growing role in their own evolution and the creation of new capabilities.
3. Bandwidth Expansion
The core architectural improvements outlined in this document directly address the bandwidth limitations of current systems. By systematically expanding memory, knowledge, and reasoning bandwidth through:
More efficient information flow between systems
Contextual compression that preserves critical details
Quantized problem decomposition that maintains coherence
High-dimensional bridges between architectural components
We enable agents to tackle increasingly complex, long-horizon problems with greater autonomy and reliability.
Measuring Progress
Our primary benchmark for success combines two critical metrics:
Research-to-Implementation Velocity: How quickly we can transform domain insights into effective evolutionary chambers
Evolutionary Efficiency: How effectively agents improve within those chambers, particularly the percentage of improvement that comes from recursive processes
Together, these metrics provide a comprehensive view of our system's ability to rapidly evolve in response to market demands while becoming increasingly self-improving over time.
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