Building AI Agents
Eight steps, and live examples of what they look like in motion.
Agents, the Okavyx way
An agent doesn't need to be AI all the way down. We build agents as AI plus automation: the AI handles judgement (classifying, choosing, replying), automation handles the rest (routing, lookups, formatting). You pay for tokens only where they earn their keep.
We can build on self-hosted n8n, so your data stays in your environment. Only the payload you send to the AI crosses that boundary, and you decide what that is.
The result: agents that are faster, dramatically cheaper, and easier to debug when something changes.
Agent Demo: Inbound Lead Qualifier
A new email arrives in Outlook. The agent decides if it's a legit lead, and if so, replies, logs it, and notifies the team.
How to Build an AI Agent
Eight steps from idea to production.
Define Purpose & Scope
- Use case
- User needs
- Success criteria
- Constraints
System Prompt Design
- Goals
- Role / Persona
- Instructions
- Guardrails
Choose LLM
- Base model
- Parameters
- Context window
- Cost / latency
Tools & Integrations
- Simple (local)
- API (web, apps, data)
- MCP server
- AI agent as a tool
- Custom functions
Memory Systems
- Episodic (conversation)
- Working memory
- Vector database
- SQL / structured DB
- File storage
Orchestration
- Routes / workflows
- Triggers
- Parameters
- Message queues
- Agent2Agent
- Error handling
User Interface
- Chat interface
- Web app
- API endpoint
- Slack / Discord bot
Testing & Evals
- Unit tests
- Latency testing
- Quality metrics
- Iterate & improve
Agent Demo: Image Battle Royale
Receives a headline, searches five stock-image providers in parallel, and an AI Vision Judge picks the best match.
Got a workflow ripe for an agent?
We design agents around the cheapest tool for each job. Tell us what's eating your team's time and we'll show you what's possible.
Let's build your first agent