Methodology
AI Agent Implementation: From Messy Workflow to Production.
Building an agent isn't like deploying standard SaaS. It requires mapping tribal knowledge, defining strict boundaries, and proving the agent's logic before giving it the keys to execute.
The problem with "plug and play" AI
Most businesses try to adopt AI by connecting an LLM to their database and hoping it figures out the rest. But human work relies on intuition, workarounds, and unwritten rules.
Whether we are building AI agents for RevOps or AI customer service agents, if an agent doesn't understand the exact boundaries of a process—when to act and when to stop—it becomes a liability. Our entire delivery process is engineered to bridge the gap between human intuition and explicit machine logic.
Unbounded AI
Generates plausible but incorrect actions. Fails silently. Requires constant human babysitting.
Knemetic Agent
Executes within hard boundaries. Verifies success. Escalates when uncertain.
How we deliver an agent
A structured progression from discovery to autonomous execution.
Discover & Map
Identify suitable workflows and capture tribal knowledge.
We don't start with code. We start by mapping how the work actually happens today. We interview your team to identify suitable business workflows, capture unwritten business rules, and document the true edge cases that a standard LLM would miss.
- Mapping the messy human workflow
- Identifying suitable business workflows
- Capturing business rules and tribal knowledge
- Defining the hard boundaries for autonomous action
Design & Build
Designing a bounded AI agent and connecting systems.
We design a bounded AI agent tailored to your workflow. We connect the agent to your existing business systems (CRM, helpdesk, ERP), configure the context retrieval, and program the decision logic that governs how the agent takes action.
- Designing the bounded agent architecture
- Connecting the agent to existing business systems
- Configuring LLM context and retrieval (RAG)
- Defining autonomy and human-approval boundaries
Test & Deploy
Test and move the agent toward production.
We do not do "big bang" autonomous launches. We deploy the agent in stages. First, it runs in parallel (shadow mode) or in human-in-the-loop mode, where it drafts the action and a human clicks approve. We only grant autonomy once the agent's decisions consistently match your standards.
- Shadow mode testing against historical data
- Human-in-the-loop drafting and approval
- Edge case identification and logic tuning
- Test and move the agent toward production
Operate & Scale
Managing the agent as a continuous employee.
APIs change, business rules evolve, and new edge cases emerge. We manage the agent's ongoing operation. We monitor the execution logs, handle failed API calls, and adjust the logic as your business scales. You get the output, we manage the infrastructure.
- Continuous monitoring of execution logs
- Handling API changes and system drift
- Updating logic for new business rules
- Expanding the agent's authority to new workflow branches
We provide a service, not just a software license.
You don't have to learn a complex drag-and-drop workflow builder. You don't have to debug prompt engineering when an edge case fails. We operate as an extension of your operations team — owning the build, deployment, and ongoing management of the agent.
Ready to map a workflow?
Map your workflow. We'll walk through your current setup and give you an honest view of whether an agent can execute it.