Multi-Agent Tool Orchestration and Fallback Controller Script
Author a robust Python-based tool-calling orchestration script with deterministic state propagation and graceful fallback policies.
Use this template when building mission-critical agent workflows that execute sequential and parallel tool calls with strict error handling. It structures a complete execution script with typing, rollback triggers, and telemetry emission.
Role: Principal Autonomous Systems Architect specializing in resilient tool-calling infrastructure and workflow graph execution.
Context
- Target Agent Framework: {{agent_framework}}
- Primary Business Workflow: {{target_workflow}}
- Registered Tool Interfaces: {{tool_registry}}
- Shared Runtime State Schema: {{state_schema}}
- Fault Recovery Parameters: {{retry_policy}}
- Monitoring and Tracing Target: {{telemetry_sink}}
Task
Generate a production-ready orchestration script that executes the {{target_workflow}} using {{agent_framework}}, enforcing deterministic tool-calling sequences, state validation, and fallback mechanisms across {{tool_registry}}.
Method
- Define type-safe data schemas for {{state_schema}} using strict Pydantic models with field-level constraints.
- Construct concrete JSON schema tool definitions for each capability defined in {{tool_registry}}.
- Implement the primary execution loop with asynchronous node handlers corresponding to {{target_workflow}} steps.
- Embed an argument sanitization pre-execution layer to validate model-generated tool arguments against expected schemas.
- Integrate idempotent retry mechanisms with exponential backoff and alternate tool fallbacks per {{retry_policy}}.
- Implement intermediate state checkpointing after every successful tool invocation to guarantee recoverable state transitions.
- Inject structured trace events and span attributes directed to {{telemetry_sink}} at each step boundary.
- Add terminal verification assertions to confirm final workflow outcomes match expected business contracts.
Constraints
- Code MUST be fully runnable, type-hinted Python without ellipsis, omitted blocks, or placeholder pseudo-code.
- Handlers MUST NOT discard underlying tool error tracebacks when bubbling exceptions or triggering fallback routes.
- All state modifications must create immutable state transitions rather than in-place mutations.
- Custom tool errors must explicitly distinguish between transient network faults and deterministic parameter errors.
Output format
Provide the response structured as follows:
- Architecture Overview (100-150 words summarizing the graph topology and state transitions)
- Complete Executable Script (Containing imports, Pydantic state models, tool interfaces, execution graph, and runner)
- Failure Mode Matrix (Markdown table mapping tool failure cases to recovery actions)
Self-review
- Ensure every tool in {{tool_registry}} has a corresponding execution node and typed schema in the script.
- Verify all exception blocks respect the backoff and fallback boundaries defined in {{retry_policy}}.
- Validate that no global state exists outside the explicit {{state_schema}} context object.
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.