Key takeaways
- Deploys coding agents (Claude Code, OpenCode, Codex) as team-accessible bots in Slack and Discord
- Enables non-technical team members to interact with AI agents via plain English in chat
- MCP and skills support allows custom agent capabilities (database queries, code reviews, production debugging)
FAQ
What is eksec.ai?
eksec.ai is a team agent platform that deploys Claude Code, OpenCode, or Codex as accessible bots in Slack or Discord, letting anyone on your team interact with coding agents via chat.
What coding agents does eksec support?
eksec supports Claude Code, OpenCode, and Codex as backend 'harnesses' that power the deployed agents.
Who competes with eksec?
Competitors include Runbear (no-code agents for Slack/Teams), Tembo (coding agent orchestration), Devin (AI software engineer with Slack integration), and OpenWork (open-source team agent alternative).
Executive Summary
eksec.ai is a team agent deployment platform that lets organizations "bake an agent and share it with your team."[1] The product takes underlying coding agents — Claude Code, OpenCode, or Codex — and deploys them as accessible team bots in Slack, Discord, or via API. This enables non-technical team members to interact with sophisticated AI agents using plain English in the chat tools they already use.
| Attribute | Value |
|---|---|
| Company | eksec |
| Founded | ~2025 (estimated) |
| Funding | Undisclosed |
| Employees | Small team (estimated fewer than 10) |
| Headquarters | Unknown |
Product Overview
eksec positions itself as the deployment layer for team-accessible AI agents. Rather than building another coding agent, eksec focuses on making existing agents (Claude Code, OpenCode, Codex) accessible to entire teams through familiar chat interfaces.[1]
Key Capabilities
| Capability | Description |
|---|---|
| Agent Harness Selection | Choose Claude Code, OpenCode, or Codex as your backend |
| MCP Integration | Add Model Context Protocol servers for database access, API calls, etc. |
| Skills & Rules | Configure agent capabilities and constraints |
| Slack/Discord Deployment | One-click deployment to team chat platforms |
| API Access | Integrate anywhere via REST API |
Product Surfaces / Editions
| Surface | Description | Availability |
|---|---|---|
| Slack Bot | Team-accessible agent in Slack channels and DMs | GA |
| Discord Bot | Team-accessible agent in Discord servers | GA |
| API | REST API for custom integrations | GA |
Technical Architecture
eksec operates as a thin orchestration layer between chat platforms and coding agents:
Slack/Discord → eksec → Claude Code / OpenCode / Codex
↓
MCP Servers (databases, tools)
Setup Flow
- Choose your base — Select Claude Code, OpenCode, or Codex as the underlying agent harness
- Set it up — Configure MCPs, skills, and rules to control agent capabilities
- Share it — Connect to Slack, Discord, or use the API[1]
Key Technical Details
| Aspect | Detail |
|---|---|
| Deployment | Hosted SaaS |
| Supported Agents | Claude Code, OpenCode[2], Codex |
| Extensibility | MCP servers, custom skills |
| Integrations | Slack, Discord, REST API |
Use Cases
eksec highlights three primary use cases on their website:[1]
Data Analyst
Connect a read-only database or data warehouse. Team members ask questions in plain English and get insights without SQL knowledge — no tickets, no waiting.
AI SRE
Wire up logs, database, and codebase. When production issues occur, team members can investigate in real-time through chat. The agent traces root causes and can even submit fix PRs.
Code Reviewer
Trigger the agent from CI on every pull request. It reviews diffs, leaves inline GitHub comments, and catches issues before human reviewers — without adding another SaaS dashboard.
Strengths
- Non-technical accessibility — Anyone on the team can interact with coding agents via Slack/Discord, democratizing AI assistance beyond developers
- Agent flexibility — Not locked to one provider; choose between Claude Code, OpenCode, or Codex based on task needs
- MCP-native — Built-in support for Model Context Protocol enables database queries, API calls, and custom tool access
- Minimal setup friction — Three-step process from signup to deployed team agent
- Customer validation — Testimonials from Yespark (French parking platform) highlight real production usage[1]
Cautions
- Limited public information — No public pricing, no documentation site, minimal company details available
- Early-stage viability risk — Funding status and team size unknown; may have sustainability concerns
- Security questions — Deploying agents with database access to entire teams raises access control concerns not addressed publicly
- No GitHub/GitLab integration — Unlike Tembo[3] or Devin[4], eksec doesn't appear to integrate with source control platforms directly
- Narrow use case — Focused specifically on team chat deployment; lacks the broader orchestration features of competitors
Pricing & Licensing
| Tier | Price | Includes |
|---|---|---|
| Free | $0 | Unknown limits |
| Pro | Unknown | Unknown |
| Enterprise | Unknown | Unknown |
Pricing information is not publicly available. Users must sign up and try the product to discover pricing.
Competitive Positioning
Direct Competitors
| Competitor | Differentiation |
|---|---|
| Runbear[5] | Runbear is no-code with fixed LLM backends; eksec uses full coding agents |
| Tembo[3] | Tembo focuses on developer workflows (GitHub, Linear, Sentry); eksec focuses on chat accessibility |
| Devin[4] | Devin is an autonomous engineer; eksec deploys existing agents |
| OpenWork | OpenWork is open-source and self-hosted; eksec is hosted SaaS |
When to Choose eksec Over Alternatives
- Choose eksec when: You want non-technical team members to access coding agents via Slack/Discord
- Choose Tembo when: You need developer workflow integrations (GitHub, Linear, Sentry) and multi-repo operations
- Choose Runbear when: You want no-code agent building with simpler LLM backends
- Choose Devin when: You want a fully autonomous AI engineer, not agent deployment
- Choose OpenWork when: You need open-source, self-hosted control
Ideal Customer Profile
Best fit:
- Teams where non-developers need to interact with AI agents (support, sales, ops)
- Companies wanting ad-hoc database querying without SQL training
- Organizations with Slack/Discord as primary communication
- Teams comfortable with early-stage products
Poor fit:
- Developer-centric teams that want GitHub/Linear integration
- Organizations requiring transparent pricing before evaluation
- Enterprise buyers needing SOC 2, SSO, and detailed security documentation
- Teams wanting self-hosted deployment
Viability Assessment
| Factor | Assessment |
|---|---|
| Financial Health | Unknown — no disclosed funding |
| Market Position | Niche — focused on team chat deployment |
| Innovation Pace | Unknown — limited public changelog |
| Community/Ecosystem | Limited — no public community or Discord |
| Long-term Outlook | Uncertain — early stage with minimal visibility |
eksec appears to be an early-stage product with real customer usage (Yespark testimonial) but limited public presence. The lack of transparent pricing, documentation, and company information creates evaluation friction.
Bottom Line
eksec.ai solves a specific problem: making coding agents accessible to non-technical team members via Slack and Discord. By wrapping Claude Code, OpenCode, or Codex in a chat-friendly interface, it enables ad-hoc database queries, production debugging, and code reviews without requiring users to understand the underlying AI systems.
Recommended for: Teams where non-developers need AI assistance — sales querying databases, support investigating issues, ops debugging production — and Slack/Discord is the primary workspace.
Not recommended for: Developer-focused teams wanting source control integration, organizations requiring transparent pricing and security documentation, or enterprises needing mature vendor stability.
Outlook: eksec occupies an interesting niche between no-code agent builders (Runbear) and developer orchestration platforms (Tembo). Success depends on execution and whether the "agents in chat" value proposition resonates beyond early adopters. The lack of public company information makes long-term viability assessment difficult.
Research by Ry Walker Research • methodology