AI Agentic Infrastructure Setup

Analyze the trade-offs of single-agent, multi-agent, and hierarchical architectures to justify the choice of a hierarchical model.

Describe the role of each key component (agent, tool, prompt, memory, orchestrator) and their interactions within a hierarchical agentic system.

Describe common agent communication patterns, including request-response, event-driven, and shared state.

Design a high-level hierarchical agent topology with specialized supervisor and worker agents.

Define a standardized tool schema with typed inputs and outputs using Zod for the Vercel AI SDK.

Implement a custom tool with input validation, error handling, and structured result formatting.

Implement a basic agent that can select and execute a provided tool based on a user prompt.

Craft a system prompt that precisely defines an agent's behavior, boundaries, and output format.

Design a database schema for storing tool definitions and metadata in a tool store.

Implement API endpoints for registering, updating, and deleting tools in the tool store.

Design a database schema for storing prompt templates with metadata and versioning.

Implement API endpoints for creating, retrieving, and updating prompt templates in a prompt store.

Implement dynamic tool loading from the store at agent initialization.

Resolve prompts dynamically from the store at runtime based on agent role and task type.

Implement a supervisor agent that delegates tasks to worker agents using the Vercel AI SDK.

Design delegation prompts that instruct a supervisor on how to interact with sub-agents.

Pass context and instructions from a parent agent to child agents.

Aggregate and synthesize responses from multiple child agents into a unified result.

Design an orchestrator that decomposes incoming requests into subtasks for the agent hierarchy.

Implement dynamic agent routing based on task classification.

Manage sequential execution of dependent agent tasks.

Implement parallel execution of independent agent tasks.

Maintain and propagate shared state across orchestrated agent calls.

Implement short-term memory for maintaining context within a single request lifecycle.

Implement conversation history storage and retrieval for multi-turn interactions.

Implement a token-aware truncation strategy to manage conversation history size.

Implement a summarization strategy for older messages to preserve context within token limits.

Design a memory interface that integrates with the agent hierarchy and orchestrator.

Design a tenant data model with associated configurations and metadata.

Implement a tenant store with CRUD operations for managing tenants.

Create tenant-to-agent mappings to define agent availability per tenant.

Create tenant-to-tool and tenant-to-prompt mappings to scope resource availability.

Resolve tenant context from incoming requests and inject it into the agent pipeline.

Implement retry logic with exponential backoff for LLM API calls.

Design fallback strategies when a sub-agent or tool fails.

Implement timeout handling for long-running agent tasks.

Surface structured error information for debugging and logging.

Implement circuit breaker patterns for external service dependencies.

Implement structured logging for agent decisions, tool calls, and prompt resolution.

Trace a request across multiple agents in the hierarchy using correlation IDs.

Monitor token usage and latency per agent and per request.

Diagnose a multi-agent failure scenario using the implemented logging and tracing tools.

Create a main API endpoint that accepts user requests and extracts a tenant identifier.

Implement tenant context resolution that loads tenant-specific agents, tools, and prompts from their respective stores.

Initialize the orchestrator with resolved tenant-specific tools, prompts, and agent configurations.

Wire the orchestrator's task routing to invoke hierarchical sub-agents with the resolved context.

Process the aggregated result from the orchestrator and return it as the API response.

Write a multi-stage Dockerfile to build an optimized production-ready container image for the Node.js service.

Implement environment variable configuration to manage secrets and settings for different deployment stages.

Create a docker-compose file to orchestrate the Node.js service and its database dependencies.