aipokit·Training
Building Agentic Workflows
Design, implement, and deploy AI agent systems for real business processes.
AI solutions for companies
Seminar offer
Most enterprise AI pilots fail for predictable reasons: the model is too general, the data is too messy, and the workflow stops at a chat box instead of reaching the systems where work actually happens.
This seminar is for teams that want a practical path — local or self-hosted models tuned to structured business data, wired into multi-step agent workflows that can read, reason, act, and hand off to humans when confidence drops.
We teach what we run: slim, task-specific LLMs (not “ask the internet everything” chatbots), orchestration patterns for agents that survive a second week in production, and governance that regulated environments can defend.
What you’ll work through
Slim LLMs on structured data
- When a small specialized model beats a frontier model — and when it does not
- Working with tabular data, document extracts, ERP exports, and internal knowledge bases
- Hands-on patterns with LLMware-style tooling: ingestion, retrieval, prompt assembly, and evaluation on your data shapes
- Privacy-first deployment: on-premise, VPC, or air-gapped — data stays where compliance requires it
Multi-step agent workflows
- Decomposing a business task into agent steps with clear inputs, outputs, and escalation
- Tool use against internal APIs, spreadsheets, and document stores — not demo toys
- Human-in-the-loop gates, audit trails, and confidence thresholds
- Failure modes: hallucination on sparse data, runaway loops, and how to design them out
From pilot to production
- Picking a first use case that finishes in weeks, not quarters
- Measuring usefulness beyond “the demo looked good”
- How this connects to Aipokit and the systems we build — and what transfers if you use something else
Who this is for
- IT and data leaders evaluating self-hosted AI without betting the farm on a single vendor API
- Operations and process owners who need agents that touch real structured data, not just prose summaries
- Consultants and integrators building repeatable B2B AI offerings for clients with strict data rules
No PhD required. Familiarity with your own data landscape and one painful manual workflow you’d like to automate is enough.
Format
2-day seminar (on-site or hybrid).
- Day 1: Slim LLMs — data ingestion, specialization, evaluation on structured sources; build one working retrieval workflow
- Day 2: Agent workflows — multi-step design, tool integration, human review; participants sketch an agent architecture for a case from their organization
Bring a anonymized sample dataset or process description if you can — the last afternoon is applied to your context.
Outcomes
You leave with:
- A working mental model for when local LLMs win
- A reference agent workflow you can extend
- A shortlist of next steps for your organization — scoped, governable, and honest about limits
Request a date
We run this seminar in-house for companies and as open enrollment when scheduled.
Like
Published · Updated