Writing
Context, connected.
Notes on context infrastructure: context graphs, RAG, MCP, and giving AI agents context that is current and connected.
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AI context management: keeping what your agents know current and correct
AI context management is deciding what an agent sees, keeping it current, and resolving conflicts across your tools. Here is what the job involves and how a context graph handles it.
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The MCP servers worth connecting to your agents in 2026
A practical guide to the MCP servers worth connecting to your AI agents, grouped by what they do, and how to tell a context server from a single-tool one.
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Connect Notion, Slack, and Linear to your AI in minutes
Your AI needs what lives in Notion, Slack, and Linear. Here is how to connect all three so an agent reads one current, reconciled source instead of three disconnected ones.
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How to give Claude your company context over MCP
Claude answers well but knows nothing about your company by default. Here is how to give it your real context over MCP, so it reads your tools instead of guessing.
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What is the Model Context Protocol (MCP)?
The Model Context Protocol is an open standard that lets any AI agent read external tools and data through one consistent interface. Here is what it is and why it matters.
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How to give your AI company context, without a migration project
Your AI is smart in general and blank about your company. Here is how to give it current, permitted context by connecting the tools your team already uses.
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Un'alternativa a Google Agentspace che non ti lega a un solo cloud
Agentspace è la ricerca aziendale e il layer di agenti di Google. Suda è un cervello aziendale che qualsiasi agente AI interroga via MCP, su qualsiasi stack. Ecco la differenza.
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Context graph vs vector database: which one your agent should query
A vector database retrieves the chunk closest to your query. A context graph answers from how your work connects. Here is the difference, and which one your agent actually needs.
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What is Suda AI? The company brain built as a context graph
Suda AI, Suda Company Brain, and Suda.so are the same product: context infrastructure that turns what's scattered across your tools into a living context graph any AI agent can read.
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Context graph vs RAG: the numbers on tokens, cost, and noise
How much a context graph saves over RAG, in plain figures: about 85% fewer tokens sent to the model, lower cost per answer, and less noise.
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A Dashworks alternative built for AI agents, not just chat
Dashworks is an AI assistant that searches your apps. Suda is a company brain your own agents query over MCP. Here is the difference and when each fits.
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A Google Agentspace alternative that is not tied to one cloud
Agentspace is Google's enterprise search and agent layer. Suda is a company brain any AI agent queries over MCP, on any stack. Here is the difference.
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A Guru alternative built as a company brain, not a wiki
Guru is a knowledge wiki with verification. Suda is a company brain your AI agents query directly. Here is the difference and when each one fits.
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A Microsoft Copilot alternative for context outside the Microsoft stack
Copilot is strongest inside Microsoft 365. Suda is a company brain across every tool, served to any AI agent over MCP. Here is the difference.
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A Notion AI alternative that reads across all your tools
Notion AI answers from your Notion workspace. Suda is a company brain that answers across Slack, Linear, GitHub, and 700 more. Here is the difference.
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A Perplexity Enterprise alternative for your own agents
Perplexity Enterprise is answer-engine search for people. Suda is a company brain your AI agents query over MCP. Here is the difference and when each fits.
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A Sana AI alternative built as a company brain for agents
Sana is an AI assistant and agent platform for people. Suda is a company brain your own agents query over MCP. Here is the difference and when each fits.
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Enterprise context for AI agents: the layer that makes them reliable
Enterprise AI agents fail on context, not intelligence. Here is what an enterprise context layer is, why scattered knowledge breaks agents, and how to build one.
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What is context engineering, and why it decides whether AI agents work
Context engineering is the practice of designing what an AI agent knows at the moment it acts. Here is what it means, how it differs from prompting, and how to do it.
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Context engineering vs prompt engineering: which one actually fixes bad AI answers
Prompt engineering shapes the question. Context engineering supplies the truth. Here is the difference, why context wins for agents, and how to get both right.
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What is a company brain, and how to actually build one
A company brain is a living, structured model of how your business works, assembled from your tools so people and AI agents always get the right, current answer.
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Context graph vs knowledge graph: the difference that matters for AI agents
A knowledge graph tells an AI what exists. A context graph tells it what matters now, why, and how a decision was made. Here is the difference and when you need each.
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Context graph vs RAG: why retrieval misses how work connects
RAG retrieves the chunk that looks similar. A context graph answers from how your work connects. Here is the difference, with a side-by-side comparison.
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A Glean alternative built as a context graph, not enterprise search
Glean is enterprise search. Suda is a context graph your AI agents query directly. Here is the difference and when each one fits.
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MCP context servers: giving any AI agent your real context
MCP lets any AI agent pull context from an external source. Here is what an MCP context server is, why it matters, and how Suda serves your whole context graph over it.
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How to give your AI a single source of truth across Notion, Slack, and Linear
Your context is split across Notion, Slack, and Linear, so your AI answers from a fragment. Here is how to give it one current, connected source of truth.
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What is a context graph, and why AI agents need one
A context graph is a living map of how your work connects, built so any AI agent can read it. Here is what it is, how it differs from RAG, and why agents need one.