Agentic Development Platform

The structured
intelligence layer
for AI-driven
development

DevCortex gives your AI agents the right context at the right moment — a precision MCP server that replaces chaotic vibe coding with spec-driven, traceable, production-quality delivery.

Get Early Access See how it works
dcx — MCP context session
# Initialize DevCortex MCP
$ dcx init
✔ Connected to acme-org/AcmeERP Project
✔ MCP server active on :3001/mcp

# Agent fetches REQ-042 on demand
tool: get_requirement("REQ-042")
→ title: "NextAuth.js HTTP-Only Cookies"
→ status: IN_PROGRESS
→ acceptance_criteria: [5 items]
→ open_issues: 1

# Only relevant context loaded
tokens used: 312 / 200k
signal ratio: high

$

"The smallest possible set of high-signal tokens that maximize the likelihood of some desired outcome."

Anthropic Applied AI Team  ·  Effective Context Engineering for AI Agents  ·  Sep 29, 2025
The Problem

Context rot is killing
your agentic projects

Vibe coding feels productive — until it doesn't. As chat sessions grow, AI agents spend their finite attention on stale history, repeated context, and noise. The result is the prompt and pray fix-it loop: fix, break, fix, break.

30%
Drop in task accuracy when full chat history replaces focused context
Chroma Research · Context Rot · 2025
18
Leading LLMs tested — every single one degrades with longer context
Incl. GPT-4.1, Claude 4, Gemini 2.5, Qwen3
n²
Attention complexity. Every token competes with every other token
Transformer architecture constraint
End-to-End Platform

One platform.
Every lifecycle stage.

DevCortex integrates specification, requirements, issues, and traceability into a single queryable database — delivered to your AI agent on demand via MCP. No more context gaps between disconnected tools.

📋
Specification Management
Versioned specification documents with full edit and release history. Your agent always knows which spec is authoritative for the current sprint.
Spec versioning
✅
Requirements Tracking
Structured requirements with MoSCoW priority, status workflow, user stories, and acceptance criteria — all queryable via MCP in real time.
Draft → Verified
🐛
Issue Management
Issues linked directly to requirements. Your agent retrieves only the open issues for the current task — not the entire backlog.
Req-linked issues
🔗
Traceability Matrix
Automated requirements-to-acceptance-criteria traceability with CSV export. Verify test coverage at any point in the sprint — without a separate test platform.
REQ → AC → Test
🤖
AI Assistant
In-app requirement generation seeded from your project database — not conversation history. Structured requirements with acceptance criteria from natural language.
Context-seeded
📊
Activity & Audit Log
Real-time WebSocket event stream of all agent and human activity. Full audit trail for compliance and retrospectives without consuming agent context.
Real-time stream

"Context rot — as the number of tokens in the context window increases, the model's ability to accurately recall information from that context decreases."

Anthropic Engineering  ·  Effective Context Engineering for AI Agents  ·  Sep 29, 2025
How It Works

Precision context,
on demand

01
Define your specification
Author a versioned Spec in DevCortex. Generate structured requirements with acceptance criteria using the AI assistant, seeded from your project database.
02
Connect your AI agent via MCP
Claude Code, OpenCode, or any MCP-compatible agent connects to the DevCortex server. The agent retrieves exactly the context it needs — one requirement, one task, one issue — on demand.
03
Build with structured context
The agent implements against clear acceptance criteria. No context amnesia, no repeated re-explanation, no fix-it loops. Status updates flow back into DevCortex automatically.
04
Verify with the trace matrix
The traceability matrix confirms every requirement has tested acceptance criteria. Export to CSV for audit, client acceptance, or sprint review.
MCP Context Session — live
Claude Code
→ DevCortex MCP :3001 → PostgreSQL
Active requirements — Sprint 10
REQ-068  Full-width sidebar layout Verified
REQ-069  CSS variable theming Verified
REQ-070  Information density In Progress
REQ-071  Navigation improvements In Progress
REQ-072  Sprint 9 UAT bug fixes Draft
Context loaded 412 tokens
Comparison

How DevCortex
compares

Purpose-built for the full development lifecycle — not just the IDE, not just the planning phase.

Capability DevCortex AWS Kiro Traycer OpenSpec
MCP server — just-in-time context retrieval ✔ Native ◑ Client only ✗ ✗
Structured requirements database ✔ PostgreSQL ◑ Markdown files ◑ Platform store ✗ Files only
Traceability matrix (req → AC → test) ✔ Auto-generated ✗ ✗ ✗
Issue tracking linked to requirements ✔ ✗ ◑ Tickets ✗
Works with any AI agent (not IDE-locked) ✔ Any MCP agent ✗ Kiro IDE only ◑ VS Code ext. ✔ 20+ tools
Local LLM & open-source model support ✔ ✗ ✗ ✔
Power user CLI ✔ dcx CLI ◑ Limited ✗ ✔ openspec CLI
Predictable pricing for intensive use ✔ ✗ Credit burn ◑ Capacity limits ✔ Free/OSS