AI AGENTS · HUMAN OVERSIGHT · BUSINESS WORKFLOWS

Build AI Agents Around Real Business Responsibilities.

We design focused AI assistants and agentic workflows that can classify information, research, draft, create tasks, update systems and recommend actions while keeping approvals and exceptions visible to people.

Business Event

→

AI Agent

→

Tools / Data

→

Recommendation

→

Human Approval

→

Action
AGENT EXAMPLES
MAIL

Email Agent

Classify messages, identify required action, prepare drafts and create follow-up tasks.

LEAD

Lead Qualification Agent

Enrich context, compare leads with criteria and recommend priority or next action.

R&D

Research Agent

Collect, organize and summarize relevant business information for human review.

TASK

Task / Follow-Up Agent

Turn communications and events into structured tasks, owners and due dates.

REP

Reporting Agent

Summarize operational activity, exceptions and recurring issues from structured data.

DOC

Document Processing Agent

Extract information, classify documents and route them into the correct process.

AGENT DESIGN

Agents need boundaries, context and escalation.

A useful business agent is not just a chatbot. It needs approved knowledge, defined tools, permissions, error handling, human checkpoints and a clear responsibility.

01ContextApproved information
→
02ReasonClassify or decide
→
03Use ToolsCRM, email, data
→
04EscalateHuman review where needed
WHEN TO USE AN AGENT

Use AI agents when the workflow requires interpretation—not just automation.

Rules-based automation is usually better when the input and action are predictable. An AI agent becomes useful when the workflow needs to interpret email, documents or natural language, choose among bounded actions, summarize context or prepare recommendations. The safest designs combine AI interpretation with deterministic rules, logging and human escalation.

Good fitClassification, summarization, research, drafting and structured extraction.
Use rules insteadExact calculations, known routing and predictable record updates.
Human approvalFinancial, legal, sensitive or high-impact actions.
MeasureAccuracy, exceptions, time saved and downstream business outcome.

See our AI agents vs traditional automation guide and AI-assisted task management case study.

FAQ
What is the difference between an AI agent and normal automation?

Traditional automation usually follows predefined rules. An AI agent can interpret unstructured information and make bounded recommendations or tool choices, but should still operate within defined permissions and review rules.

Should an AI agent send messages automatically?

Sometimes, but not always. Sensitive, high-value or ambiguous communication usually benefits from human approval before sending.

Can an agent work with our existing CRM?

Potentially yes, depending on the CRM, APIs, permissions and the business workflow.

Have a focused responsibility you want an AI assistant to handle?

Discuss an AI Agent