Port Context Lake vs Glean Enterprise Graph

Glean indexes your company's documents, chat, and tickets, infers a graph on top of them, and serves that context to assistants and coding agents over MCP. It does an excellent job of allowing employees to find what their company has written.  It cannot tell an agent what your systems are doing right now, or whether a change is allowed, because nothing in its model holds live state.

This guide is for platform teams and engineering leaders weighing Glean as the context layer behind AI agents in the SDLC. By the end, you will know what Glean does well, how it builds its graph, where that architecture stops, and what an engineering context layer has to do that a document index cannot.

What is Glean?

Glean is an enterprise AI platform that indexes company data and serves it to assistants and agents. It connects your apps, builds a graph over what it crawls, and answers questions with citations and permission checks. Glean now calls this layer its system of context.

Founded in 2019 by former Google search engineers, Glean started as enterprise search and grew into a platform. Today it ships Glean Assistant, an agent builder with orchestration and governance, an AI gateway that routes models and caps spend, and Glean Protect for security controls. Underneath sits Glean Enterprise Context: 275+ connectors, an Enterprise Graph that Glean describes as more than a thousand signals mapping people, content, and relationships, and a Personal Graph of each user's work. Glean MCP pushes that context into Claude Code, Cursor, Codex, Gemini, and Copilot, so the tool an engineer already uses can pull company knowledge without leaving the terminal. Booking.com, Zillow, Ericsson, and Databricks run it. 

What problem does Glean solve?

Engineering knowledge sits in places nobody can search at once. The decision lives in a Slack thread, the reason lives in a Confluence page, the fix lives in a closed ticket, and the person who knows left last year. Engineers burn hours reassembling that, and agents get a prompt window with none of it.

Three moments where it bites, each one a place a platform team already tracks a number:

An engineer picking up a change to the payments service asks in Slack who owns it, and ships based on the first answer that comes back. The cost shows up as services with no verified owner, and as the minutes or days it takes to get one.

Before a deploy, someone opens the repo, Datadog, and a Confluence page in three tabs to work out what else calls the service. Guess wrong, and a routine release becomes an incident, which lands on change failure rate.

A coding agent opens a pull request against a service, and the reviewer cannot tell whether the agent was allowed to touch that service at all. That shows up as the share of agent-authored changes that clear review without a policy exception or a revert.

How does Glean solve it?

Glean crawls your apps, indexes the content, then runs a machine learning pipeline over it to infer higher-level entities like projects, products, teams, and people, plus the relationships between them. Activity signals keep it current. Retrieval comes back ranked and permission-aware through search, chat, agents, or MCP.

Glean's engineering team published how the process layer works, and the detail matters. They capture change events from each app, normalize them into traces, then strip raw text and user identifiers and reduce each trace to coarse labels: action type, tool family, entities involved, a process tag, timing, and outcome. Similar traces get clustered, and a pattern only counts if it recurs across at least k distinct users and n independent traces, with rarer ones dropped to preserve anonymity. Edges in the resulting graph carry probabilities: message A triggered update B with probability P. What comes out is a distribution of likely paths that an agent can follow.

Run the three scenarios through that, and Glean moves two of them.

On ownership, an engineer asks in the IDE instead of Slack and gets a cited answer assembled from the service's docs, tickets, and past discussions, which beats a guess from whoever replies first. The answer is as current as the last document someone wrote.

On the blast-radius check, Glean collapses three tabs into one question and pulls the related tickets, code changes, and prior incident threads together, so the engineer reaches a hypothesis faster. What it returns is what people wrote about the dependencies, not the dependency graph itself.

On the agent pull request, Glean does not move the number. Its governance covers which agents exist and what they may retrieve, and a production change is not a retrieval.

Approaches to build your Agentic SDLC stack, and where Glean fits

Two paths lead to an engineering stack that agents can work in, and the choice is which layer you start from.

Path A: company-wide context layer first. Deploy Glean across the company, index everything, and reach engineering through MCP. What you get on day thirty is real: years of connector engineering, a permissions model that already works, and prose your engineers could never grep. What you own afterwards is the rest. Deployment state, incident state, dependencies, ownership, and the policies that gate a change are not in the model, and Glean's schema is documents in a datasource with permissions attached, so anything you add arrives through the Indexing API as more documents with custom metadata. Engineering ends up with a retrieval layer and a to-do list.

Path B: engineering-native context layer first. Start from a typed model of the estate, where services, environments, deployments, incidents, and teams are objects you define, reconciled continuously against GitHub, ArgoCD, PagerDuty, Datadog, Kubernetes, and Terraform, and every action an agent can take carries an approval path and a policy check. What you get is a layer agents can act through. What you own is the modeling work up front, and company-wide prose stays a separate problem.

Glean is Path A, and it is the strongest product on that path. The question is not whether the index is good. It is whether an index is the thing your agents need to act from.

Port (Agentic SDLC Platform) vs Glean

One architectural difference produces every row below. Glean infers its graph. Entities and relationships come out of a machine learning pipeline over crawled content and activity, and process edges carry probabilities. Port declares its graph. You define object types and relations in blueprints, integrations reconcile them against the systems that hold the truth, and an action checks them before it runs. An inference is enough to answer a question. It is never enough to authorize a change.

Bottom line: Glean tells your agent what your company has written about a service. Port tells it what that service is doing right now, and whether it is allowed to change it.

How to choose, and where to start

If your problem is that people cannot find what the company knows, buy the best index and stop reading. If your problem is that agents are starting to open pull requests, run remediations, and touch production, an index is the wrong authority to hand them, and what you need is an Agentic SDLC Platform: a declared model of the estate, reconciled against live systems, with policy on every action an agent can take.

GitHub went through exactly this reasoning. They had run scorecards for years on a platform that was rigid and expensive to maintain, and they wanted a living source of truth for inventory, ownership, and dependencies rather than a wiki that drifts out of date. Their stated reason for building it on Port was that you cannot give an AI agent access to your engineering systems without context and guardrails. Dlocal, a fintech payments platform, runs Port across GitHub, ArgoCD, Datadog, PagerDuty, and internal tools, so engineers see dependencies, ownership, and deployment impact before they ship, and that visibility gates their release decisions. Neither company ran a bake-off against Glean, and neither needed to: they were solving a different problem than search.

Glean's own engineers made the argument better than we can. They wrote that the context graph has to be owned jointly by the data layer and the orchestration layer, because separating them makes the graph and the execution drift into two versions of reality. They are right. In engineering, the orchestration layer deploys, rolls back, scaffolds, and blocks, and Glean does none of those things.

Sign up for free and model your first service, or book a demo and we will walk your own stack through the three questions in the diagram above.

FAQ

What does Glean do?

Glean indexes company data across 275+ connectors, infers a graph of entities and relationships over it, and serves permission-aware answers to people and agents through search, chat, its own agents and MCP into tools like Claude Code and Cursor.

What is an Agentic SDLC Platform?

A platform that gives agents context across planning, operations and governance, so they can reason and act across software delivery: what is running, what broke, who owns it, and what is allowed to ship.

Is Glean an Agentic SDLC Platform?

No. Glean is a company-wide context and retrieval layer. It holds no deployment, incident or dependency state, and it cannot gate a production change, so engineering orgs wire those parts in elsewhere.

What are Glean alternatives for engineering context?

Port for operational context across a heterogeneous SDLC. Atlassian's Teamwork Graph for work context if you live in Jira and Confluence. Cortex and OpsLevel for service registry use cases. Onyx and GoSearch compete with Glean on search itself.

Port vs Glean: which is right for me?

Choose Glean to make company knowledge findable. Choose Port if agents need live state, declared ownership and policy on every action. Large engineering orgs frequently run both.

When should I choose Glean?

When the pain is search across many departments, your sources are mostly documents, chat and tickets, and your AI use cases are answering and drafting rather than changing production systems.