AI Agents are powerful digital co-workers that combine generative AI with automation. To get the best results, follow these practical best practices — drawn from real-world experience and SME guidance.

Keep agents focused
  • Narrow scope: Each agent should solve one clear problem. Avoid general-purpose agents that try to do too much. For instance in a large workflows, use a supervisor agent that delegates tasks to specialized agents (e.g., Claim Supervisor → Claim Details Agent + Fraud Check Agent + Approval Agent).
  • Avoid tool overload: Keep the number of tools low (≤10 is enforced). Too many tools lead to confusion and higher failure rates.
Decide between Automation Vs. Agent
  • Use deterministic automation: Automations, processes, and API Tasks are best for steps that can be reliably scripted and maintained.
  • Use AI Agents: Best suited for reasoning, flexibility, or dynamic decision-making tasks. Do not default to agents for everything — most processes will remain standard automations.
Avoid batch processing in a single agent
  • No large tables/lists: Do not feed an agent with bulk data (e.g., 20 claim IDs).
  • Loop outside the agent: Use a process or bot to send one item at a time.
  • Example: A process iterates over rows in a claims table and calls the agent per row.
Use dummy Or validation tools

  • If your agent sometimes misses required outputs, add a tracker/validation tool.

  • This tool simply echoes back what the agent provided and flags what’s missing.

  • Agents are more likely to notice gaps and escalate to a human when such a tool is present.

Human in the loop for reliability

  • Always include a way for humans to review or complete missing data.

  • Gradual onboarding strategy:

    • Start with 100% human review of agent outputs.

    • As confidence grows, reduce review (e.g., 50%, then 10%).

    • Eventually, only exception cases are reviewed.

  • This builds trust over time and prevents over-reliance on untested agents.

Write balanced prompts
  • Be clear and concise: Too vague causes confusion; too prescriptive causes rigidity.
  • Avoid long prompts: Essay-style instructions reduce accuracy.
  • Provide context: Give just enough for tool selection and decision-making.
Structure inputs And outputs for automation
  • Use structured variables: Prefer `claim_id`, `policy_id` over single JSON strings.
  • Use JSON only when required: Apply it only if directly integrating with APIs.
  • Automation-friendly: Structured variables are easier for downstream processing.
Test and refine
  • Diverse testing: Run both happy path and edge cases.
  • Leverage governance logs: Analyze reasoning, tool calls, and data gaps.
  • Iterate quickly: Adjust prompts, outputs, and tools continuously.
Build for reliability

  • Validation checks (no nulls in required fields).

  • Add error-handling paths for missing or inconsistent data.

  • Use Data Tracker or dummy tools to improve completeness checks.

Validate configuration

Before saving an AI Agent, Automation 360 automatically validates configuration settings to ensure the agent is complete and error-free. This pre-deployment validation prevents configuration issues from surfacing at runtime and ensures only properly configured agents can be executed. You cannot run or publish the AI Agent until all validation errors are fixed.

Key validation areas:
Required fields:Model Connection, Role, Goal, Action Plan, Agent request title within End-user display, and Ending state definition must not be empty – spaces or tabs are treated as blank.
Tool configuration:
Tool description for AI and Automation file selection must contain valid values.
Variable configuration:
Each input and output variable usage instruction must be defined.