#The Change
Agentic Engineering is reshaping how we build and deploy AI systems. It emphasizes creating workflows that are not only efficient but also reliable and maintainable. This approach allows builders like you to turn complex AI prompts into structured systems that can be easily managed and scaled. By focusing on agentic principles, you can ensure that your AI workflows are robust and less prone to failure.
#Why Builders Should Care
As a builder, your primary goal is to create repeatable and reliable workflows. Agentic Engineering provides you with the tools to achieve this by defining clear inputs, outputs, and checks. This means you can avoid the pitfalls of brittle demos and instead ship maintainable systems that perform consistently over time. The ability to measure success through concrete KPIs—like cycle time and error rates—will help you align your projects with business objectives and stakeholder expectations.
#What To Do Now
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Define Your Workflow: Start by outlining the specific tasks your AI system needs to accomplish. Break these down into manageable steps with clear inputs and expected outputs.
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Implement Guardrails: Establish checks and balances within your workflows. This could involve setting up automated tests or manual review processes to catch errors before they escalate.
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Monitor Performance: Use metrics to track the reliability of your workflows. Pay attention to error rates and cycle times to identify areas for improvement.
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Iterate and Improve: Regularly revisit your workflows to refine them based on performance data. This will help you adapt to changing requirements and maintain system integrity.
#Example
Imagine you’re building an AI-driven customer support bot. Start by defining the key interactions it should handle, such as answering FAQs or escalating complex queries. Implement guardrails by setting up a review process for escalated tickets. Monitor the bot’s performance metrics, like response accuracy and user satisfaction, to ensure it meets your standards.
#What Breaks
While Agentic Engineering offers a structured approach, there are common pitfalls to watch out for:
- Lack of Clear Definitions: If your inputs and outputs are not well-defined, your workflows can become chaotic and unreliable.
- Ignoring Failure Modes: Failing to account for potential errors can lead to unexpected outcomes. Always plan for what could go wrong.
- Overcomplicating Workflows: Keep your systems as simple as possible. Complexity can introduce more points of failure.
#Copy/Paste Block
Here’s a basic template you can use to define your AI workflow:
workflow:
name: Customer Support Bot
steps:
- name: Answer FAQs
input: User question
output: FAQ response
checks:
- validate_response
- log_interaction
- name: Escalate Query
input: Complex question
output: Escalation ticket
checks:
- notify_support_team
- log_escalation#Next Step
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