The Unblocked Library

In-depth guides, comparisons, and resources on AI coding agents and developer productivity.

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The Auto-Loop Tax: Why Self-Running AI Agents Burn More Tokens, Not Fewer

Model Routing for Coding Agents: When Cheaper Models Save Money (and When They Cost More)

AI Tokenomics: How to Measure and Control What AI Coding Tools Actually Cost

How to Measure the ROI of AI Coding Tools (When Fewer Than 1 in 5 Teams Do)

DORA Metrics in the AI Era: Necessary but No Longer Sufficient

Context-Adjusted Productivity: Measuring What AI Actually Ships in Large Codebases

The AI Productivity Paradox: Why Your Devs Feel Faster but Ship the Same

How to Measure AI Productivity: The 2026 Framework for Engineering Leaders

How to Scale from One Agent to Parallel Agents Without Losing Quality

Memory MCP Servers Compared: Stash, MemPalace, Hindsight, agentmemory

How to Build a Context Layer for Your Engineering Org

The Context Infrastructure Stack: A CTO's Guide

MCP Tool Overload: A Measured Guide to Context-Window Bloat

Unblocked vs Glean vs Augment: Which Is Right for Your Engineering Team?

The 42,000-Token Problem With the GitHub MCP Server (and 4 Fixes)

The Context Layer: What It Is and Why Every AI Agent Needs One

Why Your AI Coding Agent Keeps Forgetting Your Codebase

Where Your MCP Token Budget Actually Goes: A 2026 Autopsy

Why Claude Code Keeps Reinventing Your Team's Rejected Approaches

Claude Code Context Rot: Why Accuracy Drops at 200K Tokens

Three Hard Lessons from Building Context at Scale

The AI Coding Tool ROI Reckoning: When Finance Starts Reconciling Spend with Output

Code Plus MCP Isn't a Context Engine: What a Real Reasoning Layer Actually Ingests

Building a Context Engine on Top of MCP: What's Possible and What's Missing

The 8 Levels of Agentic Engineering: An AI Maturity Model for Engineering Teams

Context Engine vs Search Layer vs Orchestration Layer: The Real Split

The 5 Criteria That Define a Context Engine (And Which Tools Meet Them)

Same Prompt, Same Model, Different Context: A Controlled Test

How to Implement Context Engineering for AI Coding Agents

Context Engine vs Knowledge Graph: When You Need Understanding, Not Just Data

How a Context Engine Actually Works: An Architecture Deep Dive

How to Evaluate a Context Engine: The Buyer's Checklist

Unblocked vs Glean: The Context Engine for Engineering Teams

When to Use MCP vs CLI for AI Agent Tools (2026 Decision Guide)

Unblocked vs Sourcegraph Cody: Code Context for Enterprises

Three Myths About Context for AI Agents (And What Actually Works)

Context Engineering: The Complete Guide for Engineering Leaders

Context Engineering vs RAG: When to Use Which Approach

ServiceNow vs Tabnine vs Unblocked: Enterprise Context Engines Compared

What Is a Context Engine? A Complete Guide for Engineering Teams

Context Engine vs Enterprise Search: Glean, Elasticsearch, and the Limits of Search

The AI Agent Doom Loop: Why Your Agent Keeps Making the Same Mistake

Satisfaction of Search: Why AI Agents Stop Before Finding the Right Answer

You Are the Context Engine (And That Doesn't Scale)

Context Engineering vs Prompt Engineering: What's the Difference?

Decision-Grade Context: Why Retrieval Isn't Enough for AI Agents

Why MCP Servers Alone Aren't Enough for Enterprise Context

Stop Babysitting Your Agents: How to Get Mergeable Code on the First Run

What Is Context Engineering? Definition, Principles, and Why It Matters

Context Engine vs RAG: Why Retrieval Alone Isn't Enough

The Context Problem Moved From People to Agents

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