Agentic AI Workflows: A Production Playbook

Demos lie. The autonomous agent that booked your flight in a YouTube video falls over the second a real user asks it to reschedule. After shipping multi-agent systems for healthcare triage at CareBow and claims automation at REDO, here is the playbook that actually survives production.

TL;DR

• An agent is just an LLM with a loop and tools. Treat it that way.
• Use the Perceive → Plan → Act → Reflect loop to architect every agent.
• Default to single-agent. Add a second agent only when there are at least two distinct skill sets the model needs to switch between.
• Build the human-in-the-loop checkpoint first, not last.
• Cost and latency will kill you before quality does. Budget both before you write the first prompt.

Stage 1: Perceive

Define every input the agent receives:

• The user prompt (raw, untrusted)
• Retrieved context (RAG)
• Tool outputs from the previous turn
• Memory from prior sessions
• System instructions

The failure mode here is context pollution — stuffing too much irrelevant text into the prompt and watching quality collapse. Your job is the opposite of "give it everything." Curate.

Stage 2: Plan

Decide how the agent reasons. Three architectures, in order of complexity:

1. Single-agent. One LLM, one prompt, a list of tools. 90% of production use cases.
2. Supervisor + workers. A planner agent that delegates to specialist agents. Use this when sub-tasks need different system prompts.
3. Open-ended multi-agent. Many agents talking to each other. Almost never the right answer outside of research demos.

Stage 3: Act

The agent calls tools. Two rules:

• Structured outputs only. Define a JSON schema for every tool input and output. Free-text agent output is a debugging nightmare.
• Tool count discipline. Past 7 tools, agents start picking the wrong one. Cluster tools into namespaces or split into sub-agents.

Stage 4: Reflect

The checkpoint that separates demo from product:

• Confidence-based routing: high-confidence outputs auto-execute, low-confidence go to a human review queue with the agent's reasoning attached.
• Failure logging: every refusal, hallucination, and tool error gets stored with the full trace.
• Feedback loop: human corrections become eval examples, which become prompt improvements.

Real Production Examples

CareBow symptom triage. The agent classifies a patient query into one of four care levels (self-care, teleconsult, in-home visit, emergency). Confidence under 0.7? Routes to a clinician with a structured context packet, not a raw LLM trace.

REDO claims automation. A classification agent processes 500+ claims/day. The agent never auto-denies — it only auto-approves clear cases or escalates with a one-paragraph explanation. Result: 40% ops cost reduction without a single wrongful denial.

Failure Modes Nobody Warns You About

1. Latency death spirals. A four-tool agent with 2-second tool latencies takes 10+ seconds. Users abandon at 3.
2. Context window creep. Agent loops accumulate context. By turn 5 you are paying for a 30k-token prompt.
3. Tool hallucination. Agents will invent tool names. Hard-validate every tool call against your schema.
4. Eval rot. Your eval set goes stale fast. Refresh 10% of it monthly.

Read Next

• The Complete Guide to AI Product Management
• RAG vs Fine-Tuning
• Glossary: Agentic AI