Build and Configure AI Agents and Workflows
An agent is a model plus instructions, tools, memory, and a control loop. Configuration quality usually matters more than model choice. Write system instructions that define role, scope, tone, refusal behavior, and output format. Keep instructions specific and testable; vague persona text is a common cause of unreliable behavior.
Understand tool/function calling: each tool needs a clear name, description, typed parameters, and error contract. The model selects tools from descriptions, so ambiguous descriptions cause wrong calls. Design for idempotency and validate tool inputs before execution. Orchestration patterns include single-call, chained prompts, routing to specialized prompts, and multi-step agent loops with planning and reflection. More autonomy means more latency, cost, and unpredictability - bound loops with max-iteration limits and termination conditions.
Memory and context management: conversation history, retrieved documents, and summarized state all consume the context window. Use summarization or structured state to keep long workflows coherent. Grounding via retrieval reduces hallucination; require citations or structured output where accuracy matters.
Traps:
- Instructions that conflict with tool descriptions or output schema.
- Unbounded agent loops with no stop condition or budget.
- Assuming the model will infer missing parameter constraints.
- Mixing planning and execution without a validation step between them.
Rule of thumb: every agent step should be independently testable with a fixed input and an asserted output. Worked example: for a support triage agent, configure a router prompt that classifies intent, a retrieval tool scoped to the knowledge base, and a handoff tool for billing disputes - each with explicit schemas and a fallback path when confidence is low.