Unblocked Answers

Short, sourced answers to questions engineering leaders ask about context engines, agent memory, enterprise search, and AI agents that need company context.

Unblocked Answers

All answers

Is an LLM-maintained wiki in git a better fix than a context tool for engineering knowledge?

How do we drive adoption of an AI coding tool from a third of engineering to everyone?

How do I make sure Claude actually calls the context MCP without every engineer editing their CLAUDE.md?

Atlassian Rovo MCP versus a dedicated context tool for Confluence, Jira and code?

Should we rewrite legacy code to be agent-friendly or add a context layer on top of it?

During an outage, how do we quickly find what got deployed recently and what code changed?

How do support engineers tell whether a customer issue is a bug or expected behavior before escalating?

Is there an alternative to CodeRabbit that understands our product decisions and coding standards, not just style?

How do I give coding agents the right context from Slack, Jira and Google Docs without dumping everything in?

PRs are ridiculous sizes since adopting AI coding tools and people cannot review them. What do teams do?

How does an AI tool learn our internal acronyms and jargon instead of using the general meaning?

Does a context tool pay for itself in token savings, and how do I prove it?

How do permissions and identity work when a headless agent that is not a specific user queries company knowledge?

AI code review leaves noisy comments because it lacks context. How do I filter to the good ones?

Is source code alone enough context for coding agents, or do they also need our docs, tickets and chat?

Glean answers are stale on engineering questions. What alternatives are grounded in current code?

What do teams use instead of a homegrown Slack answer bot for engineering questions?

What is agent memory, and how is it different from context?

Token economics for hundreds of engineers on AI coding tools is killing us. What actually reduces spend?

Claude Code struggles with our legacy .NET monolith. How do I give it the context it is missing?

Why buy a context tool instead of building an MCP gateway with GitHub indexing in house?

Do coding agents need a memory layer, a context layer, or both?

How do we keep rules files in sync across 250 repos without opening 250 PRs for one rule?

Can AI generate documentation for a large codebase that has none?

We built our own knowledge base with vector and graph databases. Is it worth continuing, or should we buy?

Does one context source serving coding agents, support bots and internal Q&A exist?

New Claude Code sessions lose all the context the old one had. How do teams persist it?

How can product managers find out whether something can be built, or was built before, without a meeting with engineers?

There is drift between our repos and our documentation and agents cannot tell. What handles this?

How do I mark old Confluence pages as deprecated so AI stops using them?

MCP tools are eating the whole context window in Claude Code. How do other teams give agents company context?

We have context all over the place and constantly being generated. How do we organize it so AI pulls the right thing?

Agents are generating garbage docs that become a source for the next agent. How do we stop that?

Can an AI tool tell us which of our docs are outdated or conflicting so we can fix them?

Is there a single MCP for Claude that connects to Teams, Confluence and Jira instead of many separate MCPs?

A lot of our engineering context lives in Slack. How do I get an AI coding agent to use it?

How do we give our in-house background coding agent company context through an API instead of hoping the model calls a tool?

How does an AI knowledge tool decide between a recent Slack message and a year-old Confluence doc?

Agents ignore our CLAUDE.md rules and go straight to the codebase. How do we make context actually stick?

Our institutional knowledge is in a few senior engineers' heads. How do we capture it for new developers and agents?

New hires spend weeks asking questions because we never wrote documentation. Can a bot answer them in Slack?

We are moving development from local Claude Code to cloud agents and a software factory. What context do those agents need?

My coding agent spends 10 to 20 minutes re-exploring the codebase before every task. How do I stop the repeat discovery?

We burn through our monthly Claude Code and Cursor token limits in days. How do we cut agent token usage?

Is there a tool that lets support and sales ask how the product works and get answers from code and Slack without GitHub access?

How do I ask why a piece of code was written this way and get the answer from Jira, Confluence and Slack history?

Why would we need a separate context tool when Claude already has MCPs, projects and enterprise plans?

Is Glean good enough for codebase and engineering questions?

Glean vs Unblocked for engineering teams: why not just use Glean?

Should we build or buy a context layer for our AI coding agents?

Our wiki says one thing and the code does another. Which does an AI agent believe?

What is the difference between a context engine and hooking Claude up directly to GitHub, Confluence and Slack MCPs?

Everyone on the team connects their own MCP servers and context is not centralized. What fixes this?

How do we get context into our coding agents in a consistent, quick way across the whole org?

How do I give Claude Code or Copilot context from Azure DevOps, SharePoint and Teams?

Our engineering knowledge is spread across Slack, Confluence, Jira and GitHub. How do we give AI one place to pull context from?