An AI agent is a worker with a job, tools, and a supervisor
In production, an agent is not a chatbot with your logo. It receives a trigger, gathers allowed context, chooses among tools you listed, and either completes a low-risk action or waits for a person. We build those workers against your APIs and rules. We will not sell an agent that can “do anything in the business.” Anything is how you get a bad refund, a leaked record, or a confident email that is wrong.
The loop we actually implement
Trigger → understand (retrieve, classify) → agent (plan within limits) → business rules → tools/APIs → action → human review when the action is expensive → complete → analyze. That sequence is the product. The model is one component inside it, often via OpenAI or a similar vendor.
Problems this solution is built to address
- A chatbot that cannot do the job
It talks. It cannot update the record. Staff still copy-paste. Tool calling is the missing layer.
- Autonomous dreams, no policy
Someone wants it to email customers unattended. We will insist on allow-lists, templates and review until the error cost is understood.
- No trace of what happened
You cannot debug or defend a decision. Runs must be logged: context, tools, outputs, approver.
- One mega-agent
Support, finance and hiring in one prompt. Split by job. Smaller tool sets fail more safely.
- Permissions ignored
The model sees data the user should not. Retrieval and tools must honor the same ACLs as the app.
- Cost and latency surprises
Uncapped loops and huge contexts. Budgets, timeouts and step limits belong in the runtime.
What we ship as an agent system
- Job-specific agents
One primary outcome per agent: triage, draft, extract, route, not a general employee replacement story.
- Tool registry
Typed tools with validation, timeouts and idempotency where writes happen.
- Policy layer
Hard stops in code: amounts, destinations, PII, working hours, tenant isolation.
- Review console
Approve, edit, reject. The human sees the proposed action, not only a chat bubble.
- Evaluation harness
Golden tasks you can re-run when the model or prompt changes.
- Kill switch and metrics
Disable the agent. Watch failure rate, review time, and vendor spend.
Use cases
- Inbox and ticket triage
Classify, draft, suggest assignee, send stays human until you prove otherwise.
- CRM hygiene
Parse a form or email into fields, flag duplicates, never invent a deal stage you did not allow.
- Content and YouTube stages
Research or draft inside a pipeline that cannot publish alone.
- Internal engineering ops
Agents that open issues or summarize logs with repo and identity constraints.
How an agent project runs
- Discovery
Name the trigger, the write actions, the forbidden actions, and the data the model may see.
- Scope
One agent, a tool list, a review policy, and an evaluation set you will help label.
- Architecture
Runtime, secrets, tenancy, logging store, and fallbacks when the vendor errors.
- Shadow mode
The agent proposes; humans still act. Compare. Tighten tools before enabling writes.
- Supervised production
Low-risk auto, high-risk review. Iterate on real traces. Keep the kill switch obvious.
Give the agent a job description
Trigger, tools it may call, tools it must never call, and who supervises. We will tell you if you need an agent, a classifier, or a plain workflow.