Why Agents, Not Just Chatbots

Regular chatbot

Single-turn replies

Answers one question at a time

No tool access

Can't call APIs or update records

No guardrails

No approval gates or rollbacks

Black-box responses

Can't audit what it did or why

AI Agent

Multi-step reasoning

Breaks tasks into steps, keeps going

Tool & API use

Acts inside your real systems

Guardrails built in

Approval gates for sensitive actions

Fully observable

Every step logged and auditable

What We Build

01

Sales Ops Agents:

Research leads, enrich CRM records, draft follow-ups, and flag deals that need human attention.

02

Support Resolution Agents:

Diagnose issues by checking order systems, logs, or account data, then take corrective action or escalate with context.

03

Data Processing Agents:

Pull data from multiple sources, reconcile it, and produce reports without a human stitching it together manually.

04

DevOps & Monitoring Agents:

Watch systems, triage alerts, and execute predefined remediation steps before paging a human.

05

Document Processing Agents:

Extract, validate, and route information from invoices, forms, or contracts into your systems automatically.

06

Multi-Agent Pipelines:

Chain specialized agents together for complex workflows — research, then draft, then review, then act.

How We Build It

1

Define the task

We map every decision and action the agent is allowed to take.

2

Wire the tools

APIs, databases, and systems connected as callable tools.

3

Add guardrails

Approval gates and rollback logic for anything sensitive.

4

Test real cases

Validated on historical scenarios before going live.

Tech We Work With

Agent Stack
LangGraph
AutoGen
CrewAI
LLM & Tool Calling
OpenAI Function Calling
Claude Tool Use
Integrations & Automation
n8n
Zapier
Backend & APIs
Python
.NET Core
Node.js
REST & GraphQL APIs
AI Agent

Want to See an Agent Actually Do Something?

We'll walk you through a live agent demo showcasing a real end-to-end workflow tailored to your business.

Frequently Asked Questions

What's the difference between an AI agent and a chatbot?

A chatbot answers within a conversation. An agent reasons through multi-step tasks and takes real actions — calling APIs, updating records, completing workflows — with or without a chat interface. See AI Chatbot Development if you just need conversational support.

Is it safe to let an AI agent take actions on its own?

We build in guardrails: defined permissions, approval gates for sensitive actions, and full logging. You decide how much autonomy the agent gets — full auto, human-in-the-loop, or anything between.

Can an agent use our existing systems and data?

Yes — agents are only as useful as the tools and data we connect them to. We typically pair agent development with a retrieval layer; see RAG Development for how that grounding works.

What happens when the agent doesn't know what to do?

It escalates to a human with full context rather than guessing or taking a risky action blind. This is a designed behavior, not a fallback failure.

How long does an agent build take?

A single-task agent with one or two tool integrations: 4–6 weeks. Multi-agent pipelines with several integrated systems: 8–14 weeks depending on complexity.

What does it cost?

Depends on the number of tools/integrations and how much autonomy the agent needs. Get a free estimate based on your actual use case.

Do you support it after launch?

Yes — ongoing monitoring of agent decisions and actions, with tuning as your systems or processes change.