Kore.ai Agent Platform

What it does

Kore.ai Agent Platform takes plain-language intent, business rules, tools, and policies from teams building AI agents, and produces agent definitions, deployed agents, and run-level traces, evaluations, and optimization signals. Its components include Arch, a built-in AI architect that generates agent systems, and ABL, a typed, schema-driven language for defining agent behavior, tools, guardrails, and handoffs.

Teams use the platform to design agents in a unified workspace with visual and code-based authoring, deploy them across channels such as web, voice, Slack, and Teams, and monitor them with tracing, evals, and insights. Autoloop continuously evaluates and revises agents against defined goals from build through production. Policies are enforced outside the language model, so agents stay within approved business, security, and compliance boundaries.

The platform is aimed at enterprises running complex, high-volume, or regulated workflows, including both business and technical leaders. Access starts with a demo request or a sign-in at agents.kore.ai. Pricing is not published on the product page, so buyers should confirm plans, integrations, deployment options, and costs directly with the vendor.

What it can do

• Build agents, workflows, and tools using visual and code-based authoring in a unified workspace
• Generate agent designs from plain-language intent with the Arch AI architect
• Define agent behavior, tools, guardrails, and handoffs in the ABL typed language
• Deploy agents across channels such as voice, web, Slack, Teams, and mobile
• Trace agent runs and test agent quality, safety, and accuracy with evals
• Optimize agents with Autoloop and monitor quality, cost, and compliance with AI Insights

Works with

• Slack
• Microsoft Teams

Example workflows

Designing an agent from intent

Input: a plain-language description of a business process. Steps: use Arch to generate an agent system with agents, workflows, tools, policies, and handoffs; review the generated ABL; test and deploy. Output: a structured, validated agent definition ready for deployment.

Improving a deployed agent

Input: production traces and defined performance goals. Steps: review traces in Observability; run Agent Evals against scenarios; let Autoloop or Arch Analysis recommend changes; validate changes against goals. Output: a revised agent version with recorded performance signals.

Before you choose

• Third-party integrations beyond the named channels (Slack, Teams) should be confirmed against your systems.
• Autoloop and Arch optimization depend on defined goals and evaluation criteria; teams must configure these to get meaningful improvements.
• The page uses both Artemis and Kore.ai Agent Platform names; confirm current product naming with the vendor before publishing.

Listed under

• Coding
• Code review and testing
• DevOps and observability
• Agent development
• Agent evals
• Agent platform
• AI agents
• Contact center
• Developers

Get started

Use the Get Demo button to book a meeting, or sign in at agents.kore.ai.

1. Sign in at agents.kore.ai or request a demo through the Get Demo button.
2. Define the agent's goals, tools, policies, and handoffs, either by describing intent to Arch or by authoring ABL directly.
3. Test the agent with Agent Evals and connect the desired channels and integrations before deploying.

Pricing is not published on the product page. Contact the vendor through the demo request to learn about plans and billing.

We couldn’t find published prices for Kore.ai Agent Platform. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• AI-native Agent Platform for the Enterprise | Kore.ai ↗www.kore.ai

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knowledge_base

Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.