agents.common.models.task_analysis.analysis¶

Main task analysis model combining all analysis components.

This module provides the comprehensive TaskAnalysis model that combines complexity assessment, solvability analysis, task decomposition, and execution strategy recommendations.

Classes¶

`AnalysisMethod`	Methods for analyzing task complexity and requirements.
`ExecutionStrategy`	Recommended execution strategy for a task.
`PlanningRequirement`	Placeholder for planning requirements.
`TaskAnalysis`	Comprehensive task analysis combining all analysis components.
`TaskComplexity`	Placeholder for task complexity assessment.
`TaskDimension`	Task dimensions for complexity assessment.

Module Contents¶

class agents.common.models.task_analysis.analysis.AnalysisMethod¶

Bases: str, enum.Enum

Methods for analyzing task complexity and requirements.

HEURISTIC¶: Rule-based heuristic analysis

PATTERN_MATCHING¶: Pattern matching against known task types

DECOMPOSITION¶: Bottom-up analysis through task decomposition

EXPERT_SYSTEM¶: Expert system with domain knowledge

MACHINE_LEARNING¶: ML-based complexity prediction

HYBRID¶: Combination of multiple methods

Initialize self. See help(type(self)) for accurate signature.

class agents.common.models.task_analysis.analysis.ExecutionStrategy(/, **data)¶

Bases: pydantic.BaseModel

Recommended execution strategy for a task.

Provides specific recommendations for how to approach task execution based on the complexity and solvability analysis.

Parameters:: data (Any)

strategy_type¶: Primary execution approach

priority_level¶: Urgency/priority level for the task

recommended_approach¶: Detailed approach description

resource_allocation¶: How to allocate resources

timeline_strategy¶: How to manage timing and sequencing

risk_mitigation¶: Risk mitigation strategies

success_factors¶: Key factors for success

fallback_options¶: Alternative approaches if primary fails

Example

strategy = ExecutionStrategy(
strategy_type="parallel_execution",
priority_level="high",
recommended_approach="Execute independent branches in parallel while managing dependencies",
resource_allocation={"computational": 0.4, "human_expert": 0.3, "time": 0.3},
timeline_strategy="front_load_critical_path",
risk_mitigation=["backup_data_sources", "expert_consultation", "iterative_validation"],
success_factors=["clear_requirements", "adequate_resources", "expert_oversight"]
)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

classmethod validate_resource_allocation(v)¶

Validate that resource allocation proportions sum to approximately 1.0.

Parameters:: v (dict[str, float]) – Resource allocation dictionary
Returns:: Validated resource allocation
Raises:: ValueError – If proportions don’t sum to approximately 1.0
Return type:: dict[str, float]

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class agents.common.models.task_analysis.analysis.PlanningRequirement(/, **data)¶

Bases: pydantic.BaseModel

Placeholder for planning requirements.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)

class agents.common.models.task_analysis.analysis.TaskAnalysis(/, **data)¶

Bases: pydantic.BaseModel

Comprehensive task analysis combining all analysis components.

This is the main model that brings together complexity assessment, solvability analysis, task decomposition, and execution recommendations into a unified analysis.

Parameters:: data (Any)

task_description¶: Original task description

domain¶: Task domain or field

analysis_method¶: Method used for analysis

complexity¶: Complexity assessment

solvability¶: Solvability assessment

decomposition¶: Task decomposition (optional)

planning¶: Planning requirements

execution_strategy¶: Recommended execution approach

analysis_timestamp¶: When analysis was performed

analysis_confidence¶: Overall confidence in the analysis

Example

# Analyze a simple research task
analysis = TaskAnalysis.analyze_task(
task_description="Find the birthday of the most recent Wimbledon winner",
domain="sports_research",
context="Factual lookup requiring web search"
)

# Analyze a complex research problem
analysis = TaskAnalysis.analyze_task(
task_description="Develop a cure for cancer through novel therapeutic approaches",
domain="medical_research",
context="Breakthrough research requiring novel discoveries"
)

print(f"Complexity: {analysis.complexity.overall_complexity}")
print(f"Solvable: {analysis.solvability.is_currently_solvable}")
print(f"Strategy: {analysis.execution_strategy.strategy_type}")

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

classmethod analyze_task(task_description, domain=None, context=None, analysis_method=AnalysisMethod.HYBRID)¶

Analyze a task and return comprehensive analysis.

This is the main entry point for task analysis. It performs heuristic analysis based on task characteristics.

Parameters:

task_description (str) – Description of the task to analyze
domain (str | None) – Optional domain specification
context (str | None) – Optional additional context
analysis_method (AnalysisMethod) – Method to use for analysis

Returns:

Complete TaskAnalysis instance

Return type:

TaskAnalysis

generate_executive_summary()¶

Generate an executive summary of the analysis.

Returns:: Formatted executive summary
Return type:: str

get_execution_recommendations()¶

Get prioritized execution recommendations.

Returns:: List of actionable recommendations
Return type:: list[str]

get_overall_assessment()¶

Get overall assessment summary.

Returns:: Dictionary with key assessment metrics
Return type:: dict[str, Any]

validate_analysis_consistency()¶

Validate that all analysis components are consistent.

Returns:: Self if validation passes
Raises:: ValueError – If analysis components are inconsistent
Return type:: TaskAnalysis

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class agents.common.models.task_analysis.analysis.TaskComplexity(/, **data)¶

Bases: pydantic.BaseModel

Placeholder for task complexity assessment.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)

get_complexity_score()¶

Get overall complexity score.

Return type:: float

class agents.common.models.task_analysis.analysis.TaskDimension¶

Bases: str, enum.Enum

Task dimensions for complexity assessment.

Initialize self. See help(type(self)) for accurate signature.