ChatCortex

ChatCortex is a framework for automated synthesis and optimization of AI agent architectures.

Instead of manually wiring LLM pipelines such as:

retrieval → LLM → verifier → tool

ChatCortex treats agent design as a multi-objective architecture search problem and automatically discovers architectures that optimize:

• Cost
• Latency
• Reliability

The project can be viewed as AutoML for AI Agents.

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Vision

Modern AI agents are typically constructed through manual orchestration of:

• Large Language Models
• Retrieval systems
• Tools and APIs
• Verification modules
• Memory systems

This approach is often:

• brittle
• expensive
• difficult to optimize
• difficult to reproduce

ChatCortex introduces a formal synthesis framework where agent architectures are automatically generated from task specifications and evaluated under system constraints.

Long-term goal:

Automated synthesis of reliable AI agents from high-level intent.

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Key Idea

Instead of designing agent pipelines manually:

Engineer → Manual Pipeline Design

ChatCortex enables:

Task Specification → Architecture Search → Pareto-Optimal Architectures

Architectures are evaluated across multiple objectives:

• minimize cost
• minimize latency
• maximize reliability

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Architecture Overview

ChatCortex is organized as a layered architecture synthesis system.

TaskSpecification
↓
CapabilityRegistry
↓
Synthesis Engine
↓
AgentGraph (DAG)
↓
Execution Engine
↓
Telemetry
↓
Evaluation Harness
↓
Pareto Optimization

Each layer isolates a specific concern in automated agent architecture synthesis.

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Core Components

ComponentMetadata

Formal representation of agent components such as:

• language models
• retrieval systems
• tools
• verification modules
• memory modules

Each component defines:

• capabilities
• cost per call
• latency
• reliability score
• privacy level

Components are immutable and purely declarative.

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CapabilityRegistry

Central registry responsible for:

• component registration
• capability filtering
• privacy constraint enforcement

The registry does not perform optimization --- it only provides valid components for synthesis.

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TaskSpecification

Defines the architecture synthesis problem.

Example:

from chatcortex import TaskSpecification

task = TaskSpecification(
    required_capabilities=[
        "retrieval",
        "generation",
        "verification"
    ],
    max_cost=0.01,
    max_latency=2000
)

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Synthesizers

ChatCortex includes multiple architecture synthesis strategies.

• HeuristicSynthesizer --- Greedy deterministic architecture construction.
• RandomSynthesizer --- Random baseline for stochastic exploration.
• BeamSynthesizer --- Budget-aware approximate architecture search.
• ExhaustiveSynthesizer --- Computes the exact Pareto frontier.
• ProgressiveParetoBeamSynthesizer (v0.4.0) --- Depth-aware beam widening improving Pareto recovery.

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AgentGraph

Agent architectures are represented as Directed Acyclic Graphs (DAGs).

Aggregated metrics:

• total cost (additive)
• total latency (sequential assumption)
• reliability (multiplicative model)

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Execution Engine

Two execution modes:

Deterministic Mode

Used for structural validation and reproducible testing.

Probabilistic Mode

Simulates real-world reliability using component success probabilities.

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Evaluation Harness

Supports:

• multiple tasks
• multiple synthesizers
• stochastic trials
• reproducible experiments

Outputs:

• average cost
• average latency
• success rate

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Multi-Objective Evaluation Metrics

• Pareto Coverage
• Hypervolume Loss
• Average Regret (cost, latency, reliability)

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Example

from chatcortex import TaskSpecification, BeamSynthesizer

task = TaskSpecification(
    required_capabilities=[
        "retrieval",
        "generation",
        "verification"
    ]
)

synth = BeamSynthesizer(beam_width=5)

architectures = synth.synthesize(task)

for arch in architectures:
    print(arch.total_cost(), arch.total_latency())

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Installation

pip install chatcortex

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Research Context

ChatCortex is a controlled experimental platform for studying automated AI agent architecture synthesis.

Research areas:

• multi-objective optimization
• architecture search
• AI agent systems
• reliability-cost tradeoffs
• AutoML-style agent design

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Experimental Validation

Tested on dense architecture spaces:

• 5-stage synthesis pipelines
• up to 95-point Pareto frontiers
• budget sweeps from 20 → 180 evaluations
• beam width sweeps from 3 → 15

Progressive Pareto Beam Widening demonstrates improved Pareto recovery compared to static beam strategies.

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Roadmap

Phase 1 --- Foundations

• component modeling
• capability registry
• task specification
• agent graph representation

Phase 2 --- Exact Optimization

• exhaustive architecture search
• exact Pareto frontier computation

Phase 3 --- Budget-Aware Search (Complete)

• beam search synthesis
• Pareto-aware pruning
• progressive beam widening

Future Directions

• graph-structured agent synthesis
• real model / tool integrations
• enterprise optimization layers

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Status

ChatCortex is currently a research framework under active development.

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License

MIT License

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Developed by Siddharth Saraswat