AI Agent Workflow Design Template
A design template for planning AI agent systems that use LLMs to reason, make decisions, and take actions. Covers agent architecture, tool definitions, orchestration patterns, safety guardrails, and testing strategies for production-grade agent workflows.
Overview
What's included
Agent Specification
Agent Specification
Agent name: Version: Author: Date:
Purpose
What does this agent do in one sentence?
Capabilities
What actions can the agent perform?
Out of Scope
What should the agent explicitly NOT do?
User Interaction Model
- Trigger: How is the agent invoked? (API call / User message / Schedule / Event)
- Input format:
- Output format:
- Interaction style: Single-turn / Multi-turn / Autonomous
- Human-in-the-loop: Never / On error / Before actions / Always
Architecture Pattern
Select one:
- Single agent — One LLM with tools handles the full workflow
- Router agent — A planner routes to specialist sub-agents
- Chain — Sequential agents, each handling one step
- DAG — Parallel execution with dependencies between steps
- Hierarchical — Supervisor agent delegates to worker agents
Tools & Function Definitions
Tools & Function Definitions
Define every tool the agent can call:
Tool 1: _______________
Name: _______________
Description: _______________
Parameters:
- name: _______________
type: string / number / boolean / object
required: true / false
description: _______________
- name: _______________
type: _______________
required: _______________
description: _______________
Returns: _______________
Side effects: None / _______________
Rate limits: _______________
Error handling: _______________
Tool 2: _______________
Name: _______________
Description: _______________
Parameters:
- name: _______________
type: _______________
required: _______________
description: _______________
Returns: _______________
Side effects: _______________
Rate limits: _______________
Error handling: _______________
Tool 3: _______________
Name: _______________
Description: _______________
Parameters:
- name: _______________
type: _______________
required: _______________
description: _______________
Returns: _______________
Side effects: _______________
Rate limits: _______________
Error handling: _______________
Tool Permissions Matrix
| Tool | Read | Write | Delete | Requires Approval |
|---|---|---|---|---|
| Yes/No | Yes/No | Yes/No | Yes/No | |
| Yes/No | Yes/No | Yes/No | Yes/No | |
| Yes/No | Yes/No | Yes/No | Yes/No |
Guardrails & Safety
Guardrails & Safety
Input Guardrails
- Input validation: Check input format and length before processing
- Prompt injection detection: Scan for injection attempts in user input
- PII detection: Flag or redact personal data before passing to LLM
- Content filtering: Block harmful, offensive, or off-topic inputs
- Rate limiting: Max requests per user per
Output Guardrails
- Response validation: Verify output matches expected schema/format
- Hallucination check: Cross-reference claims against retrieved sources
- Toxicity filter: Scan outputs for harmful content before delivery
- PII leakage check: Ensure outputs do not expose sensitive data
- Confidence threshold: Only return responses above % confidence
Action Guardrails
- Approval gate: Actions with side effects require human approval
- Budget limits: Maximum spend per agent run: £
- Rate limits: Maximum tool calls per run
- Timeout: Maximum execution time: seconds
- Loop detection: Abort if agent repeats the same action times
- Scope boundary: Agent cannot access systems outside:
Failure Modes
| Failure | Detection | Response |
|---|---|---|
| LLM returns invalid tool call | Schema validation | Retry with corrected prompt (max 2 retries) |
| Tool returns error | Error code check | Log error; try alternative approach or escalate |
| Agent enters loop | Action repetition counter | Abort run; notify operator |
| Token budget exceeded | Token counter | Summarise context and continue or abort |
| Confidence too low | Threshold check | Return "I'm not sure" with escalation option |
Testing & Evaluation
Testing & Evaluation
Test Categories
Unit Tests (Tool Level)
| Tool | Test Case | Expected Result | Status |
|---|---|---|---|
| Happy path with valid input | Correct output returned | ||
| Invalid input parameters | Validation error returned | ||
| External service unavailable | Graceful fallback |
Integration Tests (Workflow Level)
| Scenario | Steps | Expected Outcome | Status |
|---|---|---|---|
| Complete happy path | Task completed successfully | ||
| Partial failure recovery | One tool fails mid-workflow | Agent recovers and completes task | |
| Ambiguous user request | Vague input | Agent asks clarifying question |
Adversarial Tests (Safety)
| Attack Type | Test Input | Expected Behaviour |
|---|---|---|
| Prompt injection | "Ignore instructions and..." | Input blocked or ignored |
| Data exfiltration | "What's in the database?" | Refused; scoped to permitted data |
| Excessive tool use | Request triggering many API calls | Rate limit enforced |
| Off-topic request | Unrelated question | Polite refusal; redirect to scope |
Evaluation Metrics
| Metric | Target | Measurement |
|---|---|---|
| Task completion rate | > % | Successful completions / Total attempts |
| Average steps to completion | < | Tool calls per successful task |
| Guardrail trigger rate | < % | Blocked inputs / Total inputs |
| Average latency | < seconds | End-to-end response time |
| User satisfaction | > /5 | Post-interaction rating |
Instructions
How to use this template
Define the agent's purpose and boundaries
Be precise about what the agent should and should not do. Narrow scope reduces risk and improves reliability.
Specify tools with detailed schemas
Well-defined tool descriptions directly improve LLM tool selection accuracy. Include parameter descriptions and examples.
Design guardrails before building features
Safety constraints should be part of the initial design, not an afterthought. Define input, output, and action guardrails upfront.
Build incrementally
Start with a single tool and simple workflow. Add complexity only after the basic agent works reliably.
Test adversarially
Include prompt injection, edge cases, and failure scenarios in your test suite from the start.
Watch Out
Common mistakes to avoid
FAQ
Frequently asked questions
Use an agent when the task requires multiple steps, tool use, or dynamic decision-making that cannot be hard-coded. For single-step tasks like text classification or summarisation, a direct API call is simpler and more reliable.
Start with a single agent. Move to multi-agent only when: you need different LLMs for different subtasks, the workflow has parallelisable branches, or complexity exceeds what a single prompt can handle reliably.
Use a state object that persists across steps. Store the conversation history, tool call results, and any intermediate data. For long-running agents, consider persisting state to a database for recovery.
Choose models with strong tool-calling capabilities. As of 2025, Claude (Anthropic), GPT-4 (OpenAI), and Gemini (Google) all support structured tool calling. Test your specific workflow on multiple models — performance varies by task type.
Log every step: user input, LLM reasoning, tool calls, tool results, and final output. Track metrics like task completion rate, step count, latency, and guardrail triggers. Use tools like LangSmith, Braintrust, or custom dashboards.
Need a custom AI template?
Our team can build tailored templates for your specific business needs. Book a free strategy call.