What is a context layer for AI agents?

A service that connects to the systems where a company’s knowledge lives, such as code, tickets, docs and chat, turns it into context, and serves it to AI agents, usually over MCP, within each user’s permissions.

How is a context layer different from agent memory?

Agent memory stores what an agent or an app’s users tell it. A context layer reads what the organization has already written in its own systems and assembles answers from it.

Do context layers respect permissions from Slack, Jira and Google Drive?

Several aim to. Sentra says access is scoped by role and inherited from source systems, and Unblocked offers Data Shield, which is enabled per source. Check how each source is configured before rolling one out widely.

Does a small team need a context layer?

Often not. A small team usually needs a shared record of rules, decisions, tasks and handoffs more than search across years of documents. A context layer earns its place once the knowledge is too large for anyone to remember where it lives.

Context Layer for AI Agents: Sentra, Glen, Unblocked and Augment Compared

A context layer connects to the systems where your company’s knowledge already lives and serves it to every AI agent. How four of them work, what they ask of you, and where a lighter shared board fits beside them.

October 8, 20266 min read

A context layer for AI agents is a service that connects to the systems where a company’s knowledge already lives (code, pull requests, tickets, docs, chat and meetings), turns that material into context an agent can use, and serves it to any agent, usually over MCP. The agent asks a question; the layer answers from everything it can see, within the asker’s permissions, with sources. Sentra, Glen, Unblocked and Augment Context Engine are four current examples. They differ mainly in what they ingest, how they resolve conflicting information, and how they handle permissions. They are built for engineering organizations and larger companies where the knowledge exists and the problem is finding it.

A context layer is not the same as a memory engine. Mem0 or Supermemory store what an app’s users tell it; a context layer reads what your organization already wrote. Nor is it context engineering, which is the practice of deciding what goes into a model’s window; a context layer is one tool for doing that at company scale.

What every context layer does

Connects. It links to sources such as GitHub, Slack, Jira, Linear, Confluence, Google Drive and meeting tools, usually set up once by an admin.
Resolves. It indexes, links and sometimes reconciles the material, so the newest or most authoritative answer wins over an old thread.
Scopes. It decides what each person and agent may see, ideally by inheriting the permissions of the source systems.
Delivers. It answers agents through an MCP server, a CLI or an API, and often people through a web app or a Slack bot.

Sentra

Sentra describes itself as the managed organization memory (opens in a new tab): every meeting, message, document, line of code and deal compiled into permissioned context for teams and AI agents. Its own guide for agents says it resolves what it ingests at write time into one bi-temporal knowledge graph, with provenance and validity windows on each fact, contradiction and stale-fact detection, and role-scoped access inherited from source systems.

Agents connect over MCP or REST, and sign-up is self-serve. One structural detail is worth knowing: the public API is read-oriented. Memory is written by ingesting connected systems, not by an agent calling a write endpoint. Sentra’s use cases span engineering, sales, customer success and executive work, so it is aimed at the whole company rather than only developers.

Glen

Glen (opens in a new tab) describes itself as “Agents that get smarter as your company works.” It captures agent sessions from Claude Code, Codex and Cursor alongside Slack, GitHub, tickets, calls and docs, synthesizes them into one store, and injects relevant prior work into new prompts automatically. People and agents query it from Slack, any MCP client, a command-line tool or a web app, and its pull request reviewer queries clones of the agents that made a change to explain why.

Two details from its homepage matter when you plan a rollout. Glen says it reads public material only, such as public Slack channels and shared team transcripts. And it onboards teams in waves from a waitlist, so access is not immediate.

Unblocked

Unblocked calls itself the context layer for agentic software development (opens in a new tab): it reasons across code, conversations, issues, docs, product and production systems so agents need less human intervention. Developers install its MCP server with an installer that sets up the coding agents it finds, such as Claude Code, Cursor, OpenCode and Windsurf, and agents then call a context_research tool that returns synthesized answers citing pull requests and discussions. There are also install guides for ChatGPT, Codex, VS Code and JetBrains IDEs.

Permissions are handled by Data Shield (opens in a new tab), which restricts answers to material the person asking is allowed to see in the source system. The documentation notes it is off by default for each source and enabled one source at a time, except Slack, where it is always on. Ask whoever sets it up to check those switches before the whole company starts asking questions.

Augment Context Engine

Augment’s Context Engine is the most code-centered of the four. Its Context Engine MCP overview (opens in a new tab) describes semantic code search, awareness of how files connect across repositories and services, and indexing of commit history, codebase patterns and external sources such as docs and tickets. It comes as a local server, run through the Auggie CLI, which indexes your working directory in real time, or a remote server connected through Augment’s GitHub App, which indexes the default branches of repositories you choose. Quickstarts cover Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Zed and others, so you can add it to the agent you already use.

Side by side

From each vendor’s own pages, October 2026

                 Sources                          Delivered by            Center of gravity
Sentra           meetings, messages, docs,        MCP, REST               whole organization
                 code, deals
Glen             agent sessions, Slack, GitHub,   MCP, CLI, Slack, web    teams using agents
                 tickets, calls, docs
Unblocked        code, PRs, issues, docs, chat,   MCP via CLI, API, web   software teams
                 production systems
Augment CE       code, commit history, docs,      MCP (local or remote)   codebases
                 tickets

Questions to ask before you connect one

Whose permissions apply? Does an answer respect what the asker can see in the source system, and is that on by default?
What does it read? Every channel, or only public ones? Personal drives, or only shared ones?
How does it handle conflict? When last month’s thread and today’s ticket disagree, which wins, and does it say so?
How is a wrong answer fixed? Usually by fixing the source. Make sure someone owns that.
Where does the data go, and who at the vendor can read it? Read the trust and security pages, not the homepage.
What does an agent query cost to run? Some bill MCP queries by usage (Augment’s documentation says its Context Engine MCP is billed on token-based pricing), so estimate before rolling out to every agent.

What a context layer does not do

A context layer reads what already exists. It does not decide anything, and it does not hold the work queue. It can tell an agent that the team argued about retry limits in March; it cannot tell the agent which task to pick up next, whether a person approved it, or which rule overrides last month’s thread. Those need a record that people write on purpose.

That is the gap fenbs fills, and it is a smaller tool on purpose. It is a web app with one kanban board for a team’s work, read and updated by people in a browser and by AI apps over MCP. People set rules on the Decisions and rules page, and every assistant reads them first through fenbs_get_context. Tasks move through To Do, Next Up, In Progress and Completed. A person can pre-approve a task for AI, and an assistant can then take it with fenbs_next_approved_task; an assistant can never pre-approve or sign off a decision itself. Each session ends by saving where it left off, and History records who changed what. It does not ingest Slack or index code. For that, use a context layer.

Put together, the two answer different questions. The context layer answers “what do we know?” from everything the company has written. The board answers “what have we decided, and what is next?” from what people chose to write down. An agent that reads both knows the history and the plan.

Who should use which

A large engineering organization whose agents keep missing knowledge buried in pull requests and Slack: Unblocked or Augment Context Engine.
A company that wants one governed memory across every function, sales and support included: Sentra.
A team that wants its agents’ own sessions to feed back into future work: Glen, once you have access.
A small team, or one with non-technical members, that needs a shared plan, rules and handoffs more than search: a shared board such as fenbs, alone or beside one of the above.

Memory versus context: agent memory vs context. Team-wide memory: AI memory for teams. Coding-agent memory tools: ByteRover alternatives. Switching between people and assistants: context switching for humans and AI agents.