AI Module 02 AGENT CREATION
Less Chatbot, More Coworker
An agent is a model with a job, a set of tools, and the judgment to use them. Done right, it answers the support ticket, updates the CRM, drafts the report, and asks a human when it should. Done wrong, it's a very confident autocomplete with database access. We build the first kind: focused agents designed, tested, and monitored like the real software they are.
One Job, Done Well
The best agents are specialists. We scope each one to a clear responsibility with clear inputs, outputs, and limits, whether that's triaging inbound leads, reconciling invoices, or answering questions from your internal docs. Narrow scope means better accuracy, easier testing, and far fewer "why did it do that?" moments.
Services Overview
- Agent Scoping
- System Prompts
- Retrieval
Tools, Not Vibes
Tool & Function Design
An agent's tools are its hands. We design small, well-described, least-privilege tools with typed inputs and validated outputs, so the model can act on your systems without being able to wander off and do something creative with production data.
Guardrails & Approvals
Anything risky, expensive, or customer-facing can pause for a human thumbs-up. Add input and output checks, rate limits, and audit logs, and you get an agent your security team can actually sign off on.
Tested Like Software
Every agent ships with an evaluation suite built from real examples, so a prompt tweak or model upgrade can't quietly break last month's behavior. In production we trace every run, track cost and quality, and flag drift before your users do. Hallucinations get treated like what they are: bugs.
Services Overview
- Eval Suites
- Tracing
- Cost Tracking
- Model Upgrades
- agent run support-triage --ticket 4821
- read ticket: "invoice shows wrong plan"
- tool billing.lookup(account=ACME-114)
- found plan mismatch from last week's upgrade
- refund over $500, asking a human first
- approved by sam. credit issued, customer replied.
An agent is only as good as its tools, its tests, and the humans watching the logs.
Contact us for more info!!
Frequently Asked QUESTIONS
What's the difference between a chatbot and an agent?
A chatbot answers questions. An agent takes actions: it calls tools, reads and writes to your systems, and works toward a goal over several steps, with guardrails and approvals where they matter.
Which models and frameworks do you build agents with?
We pick per project. We regularly build on Claude, OpenAI, and open-weight models, using provider SDKs or lightweight frameworks, and avoid heavy abstractions that make agents harder to debug. For the quick yes/no, pick-one, and how-urgent decisions inside an agent's workflow, we often use System One models like Jev, which return a typed answer and a confidence score instead of an essay.
How do you keep an agent from doing something it shouldn't?
Least-privilege tools, input and output validation, human approval for risky actions, rate limits, and full audit logs. We also red team agents before launch.
How do we know the agent is still working well next month?
Every agent ships with an evaluation suite and production tracing, so quality, cost, and failures are measured continuously instead of discovered by your customers.