Agent Orchestration Language: Technical Overview

Vision Statement

Current agent planning systems fundamentally underutilize agent capabilities by forcing them into rigid operational boxes. Agents are far more flexible and adaptable than existing orchestration layers can express or leverage. This language addresses the orchestration bottleneck by providing a type-safe, compositional framework that captures agent flexibility while maintaining compile-time guarantees about feasibility, resource consumption, and domain expertise alignment.

Core Design Principles

1. Agent Capabilities as Compositional Contracts

Agents are predefined instantiations with explicit compositional contracts rather than black-box functions. Each agent declares its domains, reasoning capabilities, data scale handling, and resource characteristics in a machine-readable contract that enables sophisticated orchestration reasoning.

2. Data Scale as First-Class Types

The type system includes primitive types that inherently carry semantic information about computational requirements and processing strategies:

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type snippet = string    // ~100 chars, inline processing
type passage = text      // ~1K chars, single-pass operations  
type document = corpus   // ~10K chars, requires chunking
type collection = archive // ~100K+ chars, distributed processing

3. Multi-Dimensional Agent Characterization

Agents are typed across multiple capability dimensions:

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type Agent<D extends domain, R extends reasoning, S extends scale[]> = {
  domain_expertise: D,     // 'medical' | 'legal' | 'technical' | 'general'
  reasoning_level: R,      // 'recall' | 'analyze' | 'synthesize' | 'expert' | 'create'
  scale_capability: S,     // [snippet, passage, document, collection]
  resource_profile: ResourceProfile,
  compositional_constraints: CompositionRules
}

Type System Architecture

Primitive Data Types

The language includes scale-aware primitives that automatically inform processing strategies:

Resource Constraint Types

Budget constraints are expressed as first-class code constructs:

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type Budget = {
  tokens: number,
  time: Duration,
  cost: Currency,
  compute: ComputeUnits
}

within_budget(tokens: 10000, time: 30s, cost: $5.00) {
  // Compiler proves workflow stays within bounds
  return compose(agent.analyzer, agent.summarizer)(input_data)
}

Agent Contract Declaration

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const agents = {
  medical_expert: Agent<{
    domains: ['medical', 'general'],
    reasoning: 'expert',
    input_types: [passage, document, collection],
    output_types: [snippet, passage, record],
    cost_model: TokenBased<0.01>,
    latency_profile: High,
    reliability: 0.95
  }>,
  
  technical_analyzer: Agent<{
    domains: ['technical'],
    reasoning: 'analyze',
    input_types: [document],
    output_types: [record<Schema>],
    cost_model: Fixed<2.00>,
    latency_profile: Low,
    reliability: 0.99
  }>
}

Composition Algebra

Core Composition Operators

Sequential Composition (>>): Pipeline processing with automatic type alignment

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let pipeline = agent.extractor >> agent.analyzer >> agent.formatter
// Type: (input: document) => formatted_output

Parallel Composition (∥): Simultaneous execution with result merging

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let consensus = agent.expert_a ∥ agent.expert_b ∥ agent.expert_c
// Resource cost: max(individual_costs)

Choice Composition (⊕): Fallback chains with preference ordering

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let fallback = agent.fast_unreliable ⊕ agent.slow_reliable
// Resource bound: cheapest_successful_path

Iterative Composition (↻): Feedback loops with convergence criteria

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let refined = agent.drafter ↻ agent.critic ↻ agent.reviser
// Resource multiplier: iteration_bounds

Conditional Composition: Runtime adaptation within type bounds

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let adaptive = input.complexity > threshold ? 
  agent.complex_reasoning : 
  agent.simple_processing

Voting Composition: Multi-agent consensus mechanisms

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let voted = vote_majority(agent.a, agent.b, agent.c)
let weighted = vote_weighted({agent.expert: 0.6, agent.novice: 0.4})

Hierarchical Composition (↗): Escalation chains by capability level

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let escalated = agent.junior ↗ agent.senior ↗ agent.expert
// Automatic escalation based on confidence thresholds

Composition Type Safety

The type system tracks multiple properties across compositions:

  1. Domain Compatibility: Ensures domain expertise alignment
  2. Reasoning Sufficiency: Verifies reasoning level requirements are met
  3. Scale Feasibility: Confirms agents can handle data scales
  4. Resource Bounds: Proves workflows stay within budget constraints
  5. Error Propagation: Tracks failure modes and recovery strategies

Compile-Time Orchestration Planning

Constraint Satisfaction

The compiler functions as a sophisticated constraint solver that:

Automatic Agent Selection

Rather than manual agent specification, the system can infer optimal agent combinations:

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function analyze_medical_case(
  patient_data: collection<medical>,
  budget: Budget
): diagnosis<expert> {
  // Compiler automatically selects appropriate medical agents
  // that can handle collection-scale data within budget constraints
}

Resource Planning

The type system performs static analysis to:

Runtime Execution Model

Contract Enforcement

Runtime monitors ensure agents operate within declared contracts:

Dynamic Adaptation

While maintaining contract bounds, agents can adapt strategies based on:

Failure Recovery

Built-in resilience patterns handle:

Implementation Considerations

Agent Integration Layer

Standardized interfaces for:

Compiler Architecture

Multi-pass compilation process:

  1. Contract Resolution: Match agent capabilities to workflow requirements
  2. Type Checking: Validate composition type safety and domain alignment
  3. Resource Analysis: Prove budget constraints and optimize allocation
  4. Execution Planning: Generate optimized execution graphs with fallback paths
  5. Code Generation: Produce executable orchestration code with monitoring

Runtime Infrastructure

Distributed execution environment supporting:

Future Extensions

Meta-Learning Capabilities

Advanced Composition Patterns

Domain-Specific Extensions

Conclusion

This orchestration language transforms agent coordination from rigid procedural programming into flexible, type-safe capability composition. By making agent capabilities, resource constraints, and domain expertise first-class citizens in the type system, it enables sophisticated reasoning about complex agent workflows while maintaining safety and predictability guarantees.

The result is an orchestration layer that can fully leverage agent flexibility while providing the reliability and cost predictability required for production systems.