↓ Skip to main content
Core-AI

AI Agents

Table of Contents
Autonomous AI systems that perform complex business tasks end-to-end — not just answer questions. Built on modern LLM agent frameworks and deployed on your infrastructure for full control and auditability.

What We Build
#

Unlike chatbots, AI Agents take action. They plan, use tools, browse internal systems, call APIs, and execute multi-step workflows with minimal human intervention. Core-AI designs agent systems that connect securely to your enterprise tools — CRMs, ticketing systems, databases, internal APIs — to automate the work that today still requires a human in the loop.

Every agent we build runs on your infrastructure, with full execution logs, role-scoped tool permissions, and the ability to require human approval for sensitive actions.

Example Agents
#

  • Research Agent — Gathers information from internal documents, the web, and structured databases; produces comprehensive briefs with cited sources.
  • Operations Agent — Connects to ticketing, ERP, and CRM systems to automate cross-platform workflows that previously required manual handoffs.
  • Data Analysis Agent — Queries internal datasets, identifies trends, and generates business-ready insights with charts and natural-language explanations.
  • Compliance Agent — Reviews documents and contracts against your policy library; flags risks for legal review.
  • DevOps Agent — Triages alerts, runs diagnostics, and proposes (or, with approval, executes) remediations.

Architecture
# 

graph LR
  Req((Request)) --> Plan((Planner))
  Plan --> Exec((Execution))
  Exec --> Sys((Systems))
  Sys --> Out((Output))

  classDef n1 fill:#3b82f6,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n2 fill:#6366f1,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n3 fill:#8b5cf6,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n4 fill:#a855f7,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n5 fill:#c084fc,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;

  class Req n1;
  class Plan n2;
  class Exec n3;
  class Sys n4;
  class Out n5;

Engineering Principles
#

  • Tool-scoped permissions — Agents are granted access only to the specific tools and APIs they need.
  • Human-in-the-loop checkpoints — Configurable approval gates before irreversible actions.
  • Full audit trail — Every decision, tool call, and output is logged for review.
  • Deterministic guardrails — Hard-coded rules sit on top of LLM judgment for compliance-critical paths.
  • Open agent frameworks — Built on well-established libraries (LangGraph, AutoGen, custom orchestrators) so you can extend the system yourself.

Related Services #

Frequently Asked Questions
#

What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An AI agent takes action. Agents can plan multi-step tasks, call external APIs, query databases, write and execute code, and chain tool calls together to complete an objective — all with minimal human intervention. We build human-in-the-loop checkpoints for any sensitive or irreversible actions.
What enterprise systems can an AI agent connect to?
Agents can connect to any system with an API or SDK: CRMs (Salesforce, HubSpot), ERP systems, ticketing platforms (Jira, ServiceNow), internal databases, communication tools (Slack, Teams), and custom internal APIs. Tool access is scoped to the minimum necessary permissions for each agent.
How do you prevent agents from taking unintended or harmful actions?
We implement multiple guardrail layers: deterministic rules for compliance-critical paths that override LLM judgment, configurable human approval gates before any irreversible action, full audit logs of every decision and tool call, and sandboxed testing environments before production deployment.
What agent frameworks do you build on?
We work with established open-source frameworks — LangGraph, AutoGen, and custom orchestrators — depending on the complexity and auditability requirements of your use case. We prefer frameworks with strong observability and human-in-the-loop support over black-box solutions.
Discuss Your AI Agent Project