AI Agents

Autonomous, tool-using AI agents that plan, decide, and act, built on your data, workflows, and business rules.

Autonomous Agents Multi-Agent Systems Tool Use RAG Agent Orchestration
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Overview

What Our AI Agents Team Delivers

Most AI projects stall at a chatbot that can only answer questions. An AI agent goes further: it can look things up, call APIs, make decisions inside guardrails you define, and complete multi-step tasks end to end, with a human checking in only where it matters.

At TGT, we design and build AI agents for real operational workflows: onboarding checks in FinTech, player support and fraud triage in iGaming, patient intake and scheduling in Healthcare, and back-office automation across industries. Every agent we ship is scoped to a clear job, given the right tools, and tested against edge cases before it touches production traffic.

We build single agents for focused tasks and multi-agent systems where specialized agents such as research, execution, and review collaborate under an orchestrator, whichever fits the complexity of the problem.

When this service helps most

  • Repetitive multi-step tasks currently done manually across tools
  • Support or operations queues where an agent can resolve tier-1 cases autonomously
  • Workflows that need an AI to take action, not just answer questions
  • Internal tools where staff need an assistant that can actually do the work
Key Capabilities
Agent use-case scoping and feasibility review
Single-agent and multi-agent orchestrator architectures
Tool/function calling and API integration
RAG pipelines for grounding agents in your data
Guardrails, approval steps, and human-in-the-loop controls
Agent monitoring, logging, and cost/performance tuning
How We Work

Our AI Agent Development Process

01
Use-Case & Task Scoping
02
Tool & Data Mapping
03
Agent Architecture Design
04
Prompt & Guardrail Engineering
05
Build & Tool Integration
06
Testing Against Edge Cases
07
Deployment & Continuous Monitoring
Tech We Use

Technology Stack

Python
LangChain
LangGraph
CrewAI
Python
LangChain
LangGraph
CrewAI
Anthropic Claude
OpenAI
Pinecone / pgvector
FastAPI
Anthropic Claude
OpenAI
Pinecone / pgvector
FastAPI
Flexible Engagements

Choose the Right Delivery Model

We keep engagement models flexible so you can start small, move fast, or scale a dedicated product team when the roadmap grows.

01

Fixed Price

Best For: Well-defined projects

Clear scope, fixed budget, defined milestones. Perfect for MVPs and Phase 1 builds.

Clear milestones & budgets
Defined requirements upfront
Ideal for MVPs and POCs
No surprise invoices
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02

Time & Material

Best For: Evolving requirements

Agile-first approach where you pay for actual hours worked. Maximum flexibility.

Agile-first approach
Pay for actual hours worked
Scale team up or down
Weekly progress reports
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03

Dedicated Team

Best For: Long-term product dev

Fully embedded engineers, daily standups, and CTO-level technical oversight.

Full-time dedicated engineers
Daily standups & demos
CTO-level oversight
Transparent monthly billing
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Common Questions

Frequently Asked Questions

Straight answers about how AI agents work, what they can safely do, and how we integrate them with your systems.

A chatbot answers questions in a conversation. An AI agent can take actions, call APIs, update systems, and complete multi-step tasks with defined autonomy and guardrails.
Yes. We integrate agents with your CRM, databases, ticketing tools, and internal APIs via tool/function calling, so the agent acts inside your real workflow instead of a sandbox.
We scope the agent's permissions tightly, add human-approval checkpoints for high-risk actions, and log every decision so it is auditable, especially for regulated FinTech and Healthcare use cases.
Ready to Build?

Ready to Build an AI Agent for Your Workflow?

Tell us the task you want automated. We'll assess feasibility and propose an architecture.