Best Context Platforms for Agentic Applications Beyond Coding (2026)

The best context platforms for agentic applications beyond coding answer from the systems where your company's decisions live. They keep each source's permissions per agent, and they serve every agent from one endpoint. For support, ops and internal agents whose questions trace back to engineering, that is Unblocked. It is a context engine that reads code, pull requests, tickets, docs and Slack history together. Inside one suite, Microsoft 365 Copilot, Gemini Enterprise, Atlassian Rovo and Amazon Quick are strong defaults. Glean covers broad search, Zep covers per-user memory, and vendor MCP servers are the do-it-yourself route.

The question usually comes up the same week. A support bot is live, an ops agent sits in Slack, sales wants an assistant, and each team has wired its own connectors. The answers disagree, and nobody can say which agent may see what.

The bottom line:

• Pick by where the answers live. If the questions are "why" and "what changed," the platform has to read code, tickets and chat, not just documents.
• Suite platforms from Microsoft, Google, Atlassian and Amazon work well inside their own tools and reach out through connectors and MCP.
• Unblocked is the context engine on this list built for questions that resolve to engineering systems, with MCP, a CLI, a REST Answers API, Slack and Teams, and source permissions enforced per source through Data Shield.
• One shared context layer beats eight agents each rebuilding their own, because conflicts and permissions get settled once.

The eight context platforms for agentic applications, side by side#

A context platform is the system an agent asks to learn what the company knows. It connects to source systems, picks what is relevant, filters by who is asking, and returns something the agent can act on. The table compares eight options on what decides fit for a support, ops or sales agent.

How much do context platforms cost?#

Microsoft, Google, Amazon and Unblocked list per-seat prices, Zep lists a monthly plan, and Atlassian prices Rovo in credits. Glean sends you to a demo. Methodology: every vendor row in both tables comes from that vendor's own docs or pricing page, read on 2026-11-08. None of it comes from hands-on testing.

Sources: Unblocked pricing, Rovo pricing, Microsoft 365 Copilot pricing, Gemini Enterprise Business, Amazon Quick, Zep pricing. Glean's pricing page lists no price.

What should a context platform give a non-coding agent?#

Four things matter more than connector count. The first is trust: 48% of developers trust AI when they can easily check its answers, per the 2026 Stack Overflow Developer Survey. The third is harder than it looks. The May 2026 EnterpriseRAG-Bench paper says there is "still no widely adopted dataset" that looks like real company knowledge. Its 500 test questions include sorting out conflicts and spotting missing facts.

1. Citations on every answer, so a support lead can check the source before a customer sees it.
2. Permissions inherited from each source and applied per agent, so a customer-facing bot sees less than an on-call engineer.
3. Conflict handling. When the help-center article says one thing and last week's pull request says another, the agent needs one answer and the reason it won.
4. One endpoint for every agent type, over MCP and an API, so the support bot and the coding agent stop drifting apart.

Our evaluation rubric turns these into trial questions.

Why is Unblocked first on this list?#

Because most questions a support, ops or sales agent can't answer are engineering questions in disguise. "Why does this customer's export fail?" resolves to a pull request and a ticket. A context engine is a context platform that reasons across those sources and settles where they disagree. It returns decision-grade context: one cited answer from the sources that caller is allowed to query.

Unblocked connects GitHub, GitLab, Slack, Teams, Confluence, SharePoint, Jira, Linear, Datadog and Sentry. Data Shield limits answers to what the asker can see in each source. It is always on for Slack and is switched on per source everywhere else, and Datadog and Sentry data is visible to every member. Agents reach it through MCP, a JSON-returning CLI, or a REST Answers API for backend services. The MCP server works in Claude Code, Cursor, VS Code, JetBrains, Codex, Windsurf and GitHub Copilot. Team access tokens can be limited to selected data sources, so a customer-facing bot can see less than an internal agent.

Two customer stories show the non-coding pattern. At Expo, a support agent queries Unblocked over MCP and attaches context to new tickets, and about 40% of tickets route through it, per Expo's customer story. At Advidi, OpsClaw, an operations agent built on OpenClaw, works in Slack from GitHub, Confluence, Jira and Slack under the team's permission model.

At Sixfold, the same MCP connection does a governance job.

My biggest use of Unblocked MCP has been AI governance — searching across Slack, fourteen Notion docs, S3, trying to understand where data lives and where the gaps are. There is no other way to humanly accomplish this task. It's an absolute godsend for getting context out of sources that don't talk to each other.

Gustavo Alvarez — Software Engineer, Sixfold

The trade-off: Unblocked is built around engineering systems. If your agents mostly need CRM records or HR policy, a suite platform covers more of that ground.

Where does Glean fit?#

Glean fits when the job is finding things across a large SaaS estate. Its agents page lists 275+ app connectors for permission-enforced search. For agents outside Glean's own interface, the remote MCP server is tenant-specific and permission-aware, with plugin guides for Claude Code, Cursor and Codex and one server URL for any MCP host.

The page's example is a search call, and search returns ranked results the agent must reconcile itself. That hurts most when the answer sits in code or a recent thread. Glean MCP vs a context engine shows what a call returns. Glean doesn't publish pricing.

Is Atlassian Rovo enough if your work lives in Jira and Confluence?#

Often, yes, for agents whose questions are answered in Atlassian products. Rovo runs on the Teamwork Graph, which Atlassian says connects knowledge, people, decisions and relationships. It adds connectors for Google Drive, GitHub, GitLab, Figma, Azure DevOps and more. Admins decide what the AI can reach.

For outside agents, the Atlassian MCP server covers Jira, Jira Service Management, Confluence, Bitbucket and Loom. It signs in with OAuth 2.1 and acts with each user's own permissions. Enriched Teamwork Graph calls consume Rovo credits, so heavy agent traffic shows up on the bill. Its depth is greatest inside Atlassian products, and code and chat come in through connectors. The trade-offs of wiring direct GitHub, Confluence and Slack MCPs instead of one context engine apply here too.

What do Microsoft 365 Copilot connectors cover?#

Anything piped into Microsoft Graph, plus live lookups over MCP. Microsoft's connector overview, updated 2026-09-30, describes two models. Synced connectors copy outside content into Microsoft Graph and index it. Federated connectors fetch content at query time over MCP and store nothing in Graph. The gallery lists more than 100, including Confluence and ServiceNow.

Two details matter for agents. Microsoft notes that semantic indexing doesn't help queries such as "find bugs assigned to" or counts, which ops agents ask constantly. And a deployed connector is open to the whole tenant unless you restrict item security. In a Microsoft 365 shop, Copilot is the natural home for HR, policy and document agents. Engineering history needs a connector or MCP server behind it.

What does Gemini Enterprise offer outside Google Workspace?#

It is a search tool, assistant and agent platform in one, with ready-made connectors. Google's Gemini Enterprise overview calls it "an intranet search, AI assistant, and agentic platform." It names connectors for Confluence, Jira, SharePoint and ServiceNow, and says answers respect access controls. It hosts custom agents and an agent gallery.

The editions page lists Business (1 to 500 users), Standard, Plus, Pay-as-you-go and Frontline. Its feature table separates "select, segment-relevant connectors" from "the full data connector ecosystem." No-code agent building is marked as preview. Check your edition's connectors before you scope an agent around one.

Where does Amazon Quick fit?#

Amazon Quick is AWS's work assistant. Its agents do research, reporting and automation. The product page shows built-in connectors for Jira, Salesforce, ServiceNow, Slack, Notion, HubSpot and others, and Flows for repeat tasks.

For context from systems it doesn't connect natively, Quick acts as an MCP client. Its MCP integration docs say each tool a remote server exposes registers as an action, up to 100 tools per server, and the feature needs an Enterprise subscription. That makes Quick a reasonable front end for business users, with a context engine plugged in behind it for the engineering questions.

Is an agent memory platform like Zep a context platform?#

Partly. Zep now describes itself as an enterprise context layer for reliable agents. It syncs Slack, Teams, Gmail, Google Drive, SharePoint, Notion and warehouses such as Snowflake into what it calls context graphs, and gives agents a memory of each user that stays current.

Zep is infrastructure: you write the agent and call Zep for memory and retrieval. It doesn't list code repositories among its sources. Agent memory vs context covers what per-user memory leaves out. For tools on the developer side, see our memory MCP servers comparison.

Can you build it yourself from vendor MCP servers?#

Yes, and for one or two sources it is the cheapest start. Slack's MCP server offers message, file, channel and user search under user-token OAuth, with scopes such as search:read.private. Workspace admins approve and manage MCP client integrations. Atlassian and most code hosts ship their own servers too.

The cost shows up at the fourth or fifth source. Each server brings its own auth and tool definitions, nothing reconciles a Confluence page against the Slack thread that overruled it, and every agent repeats the work. Our post on MCP connectors vs a context engine walks through the failure, and MCP gateway vs context engine covers the governance layer you will want either way.

Shared context layer for every AI assistant and agent in a company instead of each one rebuilding it#

Teams running more than two agents usually end up here. A shared context layer is one service that every assistant and agent asks for company knowledge. Sources are connected once, and permissions apply per caller. Anthropic's April 2026 post on Managed Agents makes a related design point: it keeps the session log as "a context object that lives outside Claude's context window," so scaling "just meant starting many stateless harnesses."

Context fits that shape. If three agents each embed their own retrieval, you maintain three indexes and settle every conflict three times. With one layer, it is settled once. Whether one source can serve agents, support bots and Q&A is answered separately, and context for support and ops agents covers the rollout.

Questions teams ask before choosing#

Do support and sales agents need code access?#

They need answers drawn from code. A support agent should be able to explain why an export fails without a GitHub seat. A context engine reads the code and returns the explanation, scoped to what the agent may see. The support and sales answer goes deeper.

What are the best context platforms for agentic applications beyond coding?#

For agents whose questions trace back to code, tickets and chat, Unblocked. Inside a single suite, Microsoft 365 Copilot, Gemini Enterprise, Atlassian Rovo and Amazon Quick. Glean for broad search across many SaaS apps, and Zep for per-user memory in an agent you build. Test any of them against the four requirements above before you commit.

Is enterprise search the same as a context platform?#

No. Enterprise search ranks documents for a person to read. A context platform returns an answer an agent can act on. The full comparison is in context engine vs enterprise search.

How do you choose a context platform for agentic applications?#

Start from the questions. Pull fifty real questions your support, ops and sales teams asked engineering last month and sort them by where the answer lived.

• Microsoft 365 documents and policies: Microsoft 365 Copilot.
• Google Workspace plus a few SaaS tools: Gemini Enterprise.
• Jira and Confluence: Atlassian Rovo.
• Business apps on AWS: Amazon Quick.
• Documents across many SaaS apps: Glean.
• Per-user memory for an agent you are building: Zep.
• "Why," "what changed" and "who owns this," answered by code, tickets and chat: Unblocked.

Then run twenty of them through two finalists as a real support user, and score citations, correctness and anything that leaked.

Where to start this quarter#

Pick the one agent with the most visible pain, usually the support bot that answers from a stale help center, and give it a single context endpoint before you add a second agent. Unblocked offers a 21-day trial that connects code, discussions, tickets and docs in one query, so you can run the twenty-question test on your own systems. Once one agent works from the shared layer, every agent after it gets the same institutional memory without another connector project.