Workflow Agents

1. Install Ollama → ollama pull llama3.1:8b (~5 GB — general-purpose, good at reasoning about workflows).
2. pip install crewai (CrewAI — multi-agent workflow orchestration) or pip install autogen (Microsoft AutoGen).
3. Define a workflow agent:

from crewai import Agent, Task, Crew
researcher = Agent(role="Researcher", goal="Find data", llm="ollama/llama3.1:8b")
writer = Agent(role="Writer", goal="Write report", llm="ollama/llama3.1:8b")
task1 = Task(description="Research the top 3 trends in local AI for 2026", agent=researcher)
task2 = Task(description="Write a 500-word executive summary of the findings", agent=writer)
crew = Crew(agents=[researcher, writer], tasks=[task1, task2])
result = crew.kickoff()

1. First workflow completion in 30-90 seconds for typical 2-3 step workflows.
2. For n8n + AI: n8n is a visual workflow automation tool (n8n.io, self-hosted). Add AI nodes that call Ollama for decision-making, text classification, content generation within automated workflows.
3. Use cases: automated report generation, customer support triage, content pipelines, data enrichment workflows.

Workflow agents are text-only and VRAM-light. Llama 3.1 8B runs at 50-80 tok/s on a used RTX 3060 12 GB (~$200-250, see /hardware/rtx-3060-12gb). A 3-step workflow completes in 30-90 seconds. For a small business automating report generation, customer email triage, and data entry: $400 handles 50-100 workflow runs/day. Pair with Ryzen 5 5600 + 16 GB DDR4 + 512 GB NVMe. Total: ~$360-405. For CPU-only: Llama 3.2 3B runs workflows at slower speed but handles simple classification/routing tasks. Workflow agents are one of the best ROI use cases for local AI — automate repetitive text tasks that eat hours daily.

Used RTX 3090 24 GB (~$700-900, see /hardware/rtx-3090). Runs CrewAI/AutoGen with Llama 3.3 70B Q4_K_M for high-quality workflow execution — better reasoning at each step, fewer hallucinations in generated reports, better task orchestration. For enterprise workflow automation (1000+ workflow runs/day): batch orchestration with multiple 8B agents in parallel on the same GPU via vLLM. Total: ~$1,800-2,200. For production workflow engines: the bottleneck is often the workflow definition and error handling, not LLM throughput. Invest in workflow design before GPU.

The mistake: Building a 10-step autonomous workflow agent that generates a report, emails it to the CEO, posts to Slack, updates the CRM, and tweets the summary — all without human review between steps. Why it fails: Each LLM step has a 5-10% chance of hallucination or error. A 10-step pipeline has a ~40-65% chance of at least one error. By step 7, an error in step 3 has cascaded: the report has wrong data, the email went to the wrong person, and the tweet is nonsense with confidential data in it. The fix: Add human-in-the-loop checkpoints between critical steps. After "generate report" → human reviews draft → approves → then "send email." After "compose tweet" → human reviews → approves → then "post." Workflow agents are force multipliers, not autonomous operators. Every output that faces a customer, executive, or public audience needs human review. Internal-only, read-only workflows (classification, routing, summarization for internal use) can be fully autonomous.