Agent Orchestration Architecture: Tool Use Patterns for Enterprise Workflows

Overview

This architecture study establishes patterns for AI agents that orchestrate actions across multiple enterprise systems—translating natural language requests into multi-step workflows with proper safeguards.

The Challenge: Defining an architecture that can:

• Understand requests and break them into tool calls
• Execute actions across different systems (Jira, internal APIs, Slack)
• Handle errors gracefully (retries, rollbacks, partial success)
• Maintain audit logs for compliance

Approach

The agent uses Claude's tool use capability to orchestrate workflows:

1. Parse requests into structured actions
2. Call tools across different systems with least-privilege access
3. Handle errors with retry logic and rollback
4. Log everything for debugging and compliance

How It Works

Example Request:

"Provision dev access for sarah@company.com, update ticket DEVOPS-4231, and notify the team lead."

Agent Workflow:

1. Plan: Break down into steps (validate user, check permissions, call provisioning API, update ticket, send notification)
2. Execute: Call tools sequentially with least-privilege credentials
3. Validate: Confirm each step succeeded before proceeding
4. Report: Return structured summary with audit trail

Architecture

• Agent Controller: LLM-powered planner (Claude) with tool-use capability
• Tool Registry: Standardized interface for each system (Jira, internal APIs, Slack, knowledge base)
• Execution Engine: Retry logic, timeout handling, partial rollback on failure
• Audit Layer: Logs every tool call (input, output, user, timestamp) for compliance

Tools Implemented

• Ticketing: Create, update, assign, close tickets in work management system
• Provisioning API: Grant/revoke access to internal systems
• Knowledge Retrieval: Search internal docs and policies
• Notification: Send Slack messages, email notifications
• Metrics: Query system health, usage stats

Patterns Established

1. Precise Tool Schema Pattern Vague tool descriptions led to hallucinated parameters. This work established standardized tool schemas with explicit examples and validation rules—reducing errors by 40%. This pattern is now the standard for all agent implementations.

2. Human-in-the-Loop Gate Pattern For high-risk actions (deleting data, granting admin access), we defined a confirmation gate that blocks execution until approved. This pattern preserves trust while enabling automation and has been adopted across agent workflows.

3. Partial Success Degradation Pattern When one tool call failed, agents would halt entirely. We established rollback logic and partial success reporting: "Completed steps 1-3, step 4 failed (retrying)." This pattern enables resilient multi-step workflows.

4. Agent Observability Framework Built a dashboard showing agent activity: success rate per tool, failure modes, execution time. This observability pattern is now required for all production agent deployments.

Technical Details

Agent Loop

1. Parse user request → extract intent + entities
2. Generate execution plan → sequence of tool calls
3. For each tool call:
   - Validate input parameters
   - Execute with timeout
   - Handle errors (retry, rollback, or escalate)
   - Log result
4. Synthesize final response with audit summary

Tool Security

• Each tool had its own service account with minimal permissions
• Tool calls included user context for authorization checks
• Rate limiting prevented abuse
• All outputs were sanitized before returning to user

Strategic Insights

This work establishes that agent orchestration is fundamentally an architecture problem, not just a prompting problem. The key insight: production agent systems require the same rigor as distributed systems—error handling, observability, and graceful degradation are not optional.

Architectural Principles Defined:

1. Tool Schema as Contract: Tools must have explicit, validated contracts. Precision in tool definitions directly correlates with agent reliability.

2. Human Gates for Irreversible Actions: Automation should accelerate workflows, not create risk. High-consequence actions require human approval gates as an architectural principle.

3. Partial Success is Success: Multi-step workflows will fail partially. Systems must be designed to report and recover from partial failures, not treat them as total failures.

4. Observability from Day One: Agent systems are black boxes by default. Comprehensive logging and dashboards must be architectural requirements, not afterthoughts.

Impact & Adoption

The patterns from this work have influenced how agent systems are built across the organization:

• Tool Schema Standard: The standardized tool schema format is now required for all agent implementations, reducing hallucination errors by 40% organization-wide.

• Human-in-the-Loop Library: The confirmation gate pattern was extracted into a reusable library, adopted by 4 teams building agent workflows.

• Agent Observability Dashboard: The observability framework became the template for monitoring all production agent systems.

Cross-Team Impact: These patterns were presented at an engineering all-hands, influencing how 3 other teams designed their agent architectures. The "partial success" pattern specifically solved a critical issue another team was facing.

Outcome

The agent system successfully automated 60% of routine provisioning and ticket management tasks, reducing average resolution time from hours to minutes. The architecture patterns established have become the foundation for how the organization builds all multi-system agent workflows.