MCP vs. RAG vs. AI Agents: Who Leads AI in 2025? |

GPT-4, Claude, and Llama have pushed the boundaries of what large language models can do—but at their core, they still rely on basic language generation.

They might sound smart, but most models still lack memory of past interactions or the ability to act autonomously on complex tasks. That’s where next-gen AI architectures come in.

Enter retrieval-augmented generation (RAG) agents, memory-context prompting (MCP) agents, and AI agents—three approaches that go beyond text prediction to deliver grounded knowledge, contextual awareness, and goal-driven action.

In this blog, we’ll break down RAG vs. MCP vs. AI agents, help you understand when to use each, and show how makes it easy to bring them together in one intelligent, scalable workspace.

RAG vs. MCP vs. AI Agents: At a Glance

Here’s a quick breakdown of how RAG fares against MCP and AI agents. Keep scrolling for detailed explanations, definitions, examples, and more!

Feature/Aspect	RAG (retrieval-augmented generation)	MCP (memory-context prompting)	AI agents
Primary goal	Provide up to date knowledge	Maintain interaction continuity	Execute tasks, solve problems
Core mechanism	Retrieve → Augment prompt → Generate	Memory → Augment prompt → Generate	Plan → Act → Observe → Iterate
Solves for	Outdated models, hallucinations	Statelessness of LLMs	Lack of action capability
Tool access	Search and retrieval engines	None required	Broad: APIs, files, apps, web, code
Architecture	LLM + retriever	LLM + memory manager	LLM + tools + memory + execution loop
Use cases	Knowledge bots, customer support, legal search	Chatbots, onboarding assistants	DevOps agents, smart schedulers, CRM workflows

TL;DR:

• RAG solves what your AI doesn’t know
• MCP solves what your AI doesn’t remember
• Agents solve what your AI can’t do—yet

The most capable AI systems often combine all three, such as Brain! Try it now! 🚀

What Is RAG (Retrieval-Augmented Generation)?

Retrieval-Augmented Generation (RAG) is an AI architecture that boosts the accuracy and relevance of LLM-generated responses by pulling in up-to-date information from external sources—like vector databases, APIs, or private docs—before generating a reply.

Instead of relying solely on what the model “remembers,” RAG fetches real-world data from a centralized knowledge store in real time to produce more grounded, reliable outputs.

By using techniques like similarity search, RAG agents ensure the most relevant data is retrieved from your knowledge store in one retrieval pass. This helps generate grounded responses by injecting retrieved context into the model’s reasoning loop.

How it works:
When a user submits a prompt, RAG first retrieves relevant content from connected data sources. This information, often pulled from retrieved documents like support articles, internal wikis, or contracts, is then added to the prompt, enriching the model’s context with real-world relevance. With this setup, the LLM generates a response based not just on its training but on actual, real-time facts.

Why it matters:
RAG dramatically reduces hallucinations by grounding outputs in retrieved data and external knowledge—without retraining the model.

It also enables access to fresh or proprietary data, again, without needing to retrain the model. Since it’s modular, you can plug it into different retrievers or even operate across multiple AI model configurations for specialized tasks.

And yes, it supports citations! The presence of citations boosts user trust by helping validate that the model is generating the correct answer with traceable sources.

An example of a RAG agent use case would be: A customer support bot using RAG that instantly pulls refund policies from your internal wiki, quotes the exact section, and provide a helpful answer in seconds.

Challenges to keep in mind:
RAG systems must be tuned carefully to retrieve the right information. They can introduce latency, and managing chunk size, embeddings, and prompt structure takes real effort—especially when trying to improve retrieval precision for high-stakes queries.

If you’re considering whether to use RAG or fine-tuning for knowledge retreival, check out this RAG vs. fine-tuning comparison guide that breaks it down clearly.

Here are some RAG examples:

• Support bots answering policy or pricing questions
• Enterprise search tools digging through internal docs
• Financial summaries using live market data
• Legal tools referencing updated case law

What Is MCP (Memory-Context Prompting)?

Memory-Context Prompting (MCP) is a technique that helps LLMs simulate memory—so they can maintain context across multiple interactions. Since these models are inherently stateless, MCP bridges the gap by feeding past interactions or relevant user data back into each new prompt.

MCP defines a lightweight model context protocol for extending memory without building complex infrastructure. Whether you’re deploying a new MCP server or integrating with an existing MCP tool, the goal remains the same: maintain context and reduce token usage.

How it works:
The system stores previous conversation turns or structured memory data. Then, when a new prompt comes in, it selects relevant pieces—using semantic search, summarization, or sliding windows—and appends that context to the latest input. The result? A response that feels aware of what’s happened before.

Why it matters:
MCP allows for more natural, multi-turn conversations. It helps AI tools remember user preferences, track progress, and support task continuity without requiring full-blown memory architectures. It’s also lightweight and relatively easy to implement, making it great for iterative or conversational workflows.

For IT teams in particular, MCP offers a flexible way to retain user context across workflows—learn more about tailored AI tools for IT professionals that combine memory, context, and automation.

As MCP adoption grows, more teams are customizing memory flows via their own MCP server to tailor response behavior to their unique business rules.

A few examples of MCP in action:

• A journaling assistant using MCP might recall that last week you wrote about burnout—and gently ask if you tried that walking break you mentioned.
• For teams needing to retain structured memory over longer workflows, MCP extend capabilities allow for modular expansion—keeping conversations consistent across tools, use cases, and time.

Challenges to keep in mind:
Token limits still apply, so the amount of memory you can include is constrained. Irrelevant or poorly selected memory can confuse the model, so a thoughtful strategy for what to retain and when to include it is essential.

Here are some MCP examples:

• Chatbots that remember user names and past interactions
• Educational tools tracking student progress
• Story-driven apps that adapt based on user behavior
• Onboarding flows that recall user history and preferences

What Are AI Agents?

AI agents take LLMs a step further—from passive responders to active doers. Instead of just generating answers, agents set goals, make decisions, take actions, and adapt based on feedback. They’re the bridge between language and automation.

Here’s what sets them apart:
An agent starts with a defined goal—say, planning a week of social media posts. It then breaks that goal into steps, uses tools like APIs or search engines, carries out tasks (like writing or scheduling content), and evaluates the outcomes.

Agents don’t just follow instructions—they reason, act, and iterate. Each decision loop is influenced by programmed or learned agent behavior, which allows agents to adapt dynamically to changing goals or constraints.

Advanced AI agents often operate within multi-agent systems, where multiple agents collaborate across specialized tasks. These autonomous agents are guided by an agent’s logic, allowing them to perform tasks autonomously while adapting to changing inputs.

For example, specialized AI agents can be trained to handle specific roles—like finance, content, or QA—within your larger workflow.

💡 Pro Tip: Test your AI agent flows in low-risk automations first (like content generation or status updates), then graduate to high-impact workflows like sprint planning or bug triage.

Why this matters:
AI agents can handle end-to-end workflows, operate across tools and environments, and reduce the need for constant human input. They’re ideal for repetitive, complex, or multi-step processes that benefit from autonomy. This also opens the door to more complex decision-making, where agents must weigh priorities, coordinate with systems, and resolve conflicts across workflows.

Curious about what this looks like in action? From marketing automation to IT troubleshooting, here are some of the most powerful AI use cases across industries that highlight how agentic systems are already transforming workflows.

Imagine a marketing agent that researches a competitor’s product launch, creates a response campaign, schedules it across platforms, and logs everything in your workspace—all without needing a human in the loop.

What’s the catch?
Because they span external systems and rely on varied tool usage, agents require more careful orchestration. They’re more complex to build and debug. You’ll need to monitor and sandbox them carefully, especially when they’re connected to critical systems. And since agents make multiple LLM calls, they can be resource-intensive.

Here are some AI agents examples:

• Dev teams automating code reviews or repo updates
• Marketing teams offloading research and campaign planning
• IT departments triaging alerts and executing fixes
• Personal agents managing calendars, reminders, or emails

Curious how different industries are applying agentic systems? Our AI use cases guide explores how AI agents are revolutionizing workflows in marketing, engineering, and operations.

🧩 Fun fact: Some AI agents can reprogram themselves on the fly based on performance feedback. That’s next-level “learn from your mistakes.”

And some AI agents use tools like ReAct to literally “think out loud,” writing their reasoning step-by-step before making a move—like journaling their thoughts before solving a puzzle.

RAG vs. MCP vs. AI Agents: Which One Should You Use?

Choosing between RAG, MCP, and AI Agents isn’t about picking a trend—it’s about aligning the right architecture with your workflow, data strategy, and end goals.

Let’s break it down with deeper technical reasoning, practical examples, and how supports each use case.

🧠 When to use RAG

RAG shines when factual accuracy, data freshness, and transparency are paramount for your application.

Use RAG when:

• You have large, frequently updated datasets (internal wikis, documentation, SOPs, product specs).
• You need traceable sources (i.e., “Where did this answer come from?”).
• You want to reduce hallucinations by grounding LLM output in real content.

Example use cases:

• An internal AI assistant that pulls answers from your company data and knowledge base hosted in Docs
• Legal teams that retrieve clauses from policy documents or contracts
• Customer support bots surfacing real-time troubleshooting info from updated docs

🚀 Advantage: Store and structure your source documents in Docs. Add AI-enhanced search with Knowledge Management and Brain to create a RAG-style assistant that generates grounded responses in real time—without needing to train a new model.

You can also explore how other teams are implementing AI tools for decision-making using RAG-like architectures to make informed, data-driven calls.

🚫 Limitation: RAG can’t reason or act—it primarily fetches and summarizes information.

🧠 When to use MCP

If conversation continuity, remembering user details, and maintaining context across interactions are key, then MCP is your technique.

Use MCP when:

• Your AI system needs to recall user preferences, previous inputs, or historical actions.
• You’re managing multi-turn conversations or decision chains.
• You want lightweight context management without building a full memory database.

Example use cases:

• AI onboarding bots that remember what the user has completed (e.g., setting up integrations).
• Personal AI productivity coaches recalling your goals and follow-ups.
• Finance tools adjusting their advice based on past user behavior.

🚀 Advantage: MCP-style memory fits naturally in through Tasks, Docs, Comments, and Activity logs. With Brain, AI can pull historical context to refine its suggestions—like who’s responsible for what, what was last discussed, and what’s next.

🚫 Limitation: MCP still relies on prompt engineering; it doesn’t typically initiate actions or learn dynamically on its own.

How AI works as an AI agent

AI agents don’t just answer questions—they observe, plan, execute, and adapt. And that’s exactly what AI is built to do.

Whether you’re managing projects, automating internal ops, or building AI-native products, gives you the perfect foundation to launch intelligent agents that work with your team—and scale without added complexity.

✅ What makes AI agentic?

To qualify as an AI agent, a system needs more than generative AI capabilities. It must integrate memory, reasoning, action, and learning within a goal-oriented workflow.

checks every box:

Capability	AI Functionality
Memory	✅ Brain remembers context across tasks, Docs, comments, and workflows
Reasoning	✅ AI interprets user intent, references historical data, and suggests optimal next steps
Planning	✅ Agents can generate and schedule tasks, goals, or reminders from simple input
Execution	✅ With Automations, agents carry out actions like updating statuses or assigning owners
Tool Use	✅ integrates with Slack, GitHub, Google Calendar, and more—AI acts across systems
Feedback Loop	✅ Activity tracking + conditional logic allows agents to react and improve over time

With integrated decision-making logic and a clean user interface, AI interprets user input and aligns it with your domain knowledge and business rules. Whether the agent is triggered by a user query or an automated workflow, its control mechanism ensures accurate outputs based on context and intent.

Let’s break this down.

🧠 Brain = memory + context awareness

Brain is the neural core of your AI agent. Unlike standalone tools that rely on shallow prompt history or external databases, Brain lives inside your workspace and understands it natively. It doesn’t just store data—it interprets it to take meaningful action.

This kind of context-awareness is a leap forward in AI and machine learning systems, where integrated memory and inference are becoming core to intelligent execution.

What that looks like in practice:

Brain can instantly recall project history, including task updates, comments, time logs, and due date changes. For instance, if a high-priority task has seen repeated delays or blockers noted in comments, it can flag the task for escalation, suggest timeline updates, or recommend redistributing work.

It also understands ownership and responsibility. Since assignees, roles, and dependencies are part of your workspace structure, you can ask:

“Who owns this?”
“Is this blocked?”
“Has anyone from design reviewed this?”

And get instant, accurate answers—no back-and-forth needed.

When it comes to meetings, Brain does more than take notes. Using Docs or the AI Notepad, it can extract key action items, assign owners, and create follow-up tasks automatically—turning conversations into structured work.

AI is a boon when it comes to onboarding. If a new teammate joins a task, Brain can proactively attach internal Docs like the brand messaging guide, design request SOP, or campaign checklists—making ramp-up seamless and fast.

🧠 Why it’s a game-changer:

Most AI tools need manual context input. Brain flips the script by embedding memory and awareness into the actual workspace. That gives your AI agent the ability to:

• Understand ongoing projects without manual training
• Maintain memory across tasks, meetings, and timelines
• React in real-time to workspace changes—without scripting or setup

All of this amplifies the AI’s ability to make intelligent contributions in real-time—without needing constant user direction. There’s no need to build custom memory systems or fine-tune a model— Brain is ready from day one.

⚙️ Automations = Where AI starts taking real action

Brain gives your agent context. Automations give it the power to execute.

While most automation systems follow simple if-this-then-that logic, ’s engine goes further. By pairing rules with AI, your workflows become dynamic systems that adapt to your team’s behavior and activity in real time.

What that looks like in practice:

Let’s say a task is marked “Needs Review.” Your agent doesn’t just ping the team—it kicks off a complete review process:

• Reassigns the task to the QA lead
• Notifies them in Slack or Microsoft Teams
• Creates a checklist with review steps based on the task type
• Sets a due date that aligns with your SLA policy

Or when an intake form is submitted, it can:

• Extract critical info like urgency, requester, and project type
• Classify the request (bug report, marketing brief, support task)
• Spin up a new project task with subtasks
• Assign stakeholders and set a start date automatically

Even bug reports become action items. If someone leaves a comment like “the site’s down,” your AI agent can:

• Detect severity using AI classification
• Update task status to “Urgent”
• Route the issue to the on-call engineer
• Trigger a checklist to log, fix, test, and deploy—all automatically

🧠 Why it’s a game-changer:

Automations scale with your workflows. Start simple with a few triggers, then add layers of logic and AI-powered actions—without writing a single line of code.

As your systems evolve, so does your AI agent. It doesn’t just follow instructions—it learns how your team works and supports you at every turn.

✍️ AI + Tasks = Creation that drives momentum

AI inside Tasks isn’t just helpful—it’s operational.

Instead of acting like a chatbox on the side, it lives inside your work and helps your team translate raw input into structured, collaborative action.

What that looks like in practice:

Summarize messy conversations
Just wrapped a long thread? AI highlights the key decisions and next steps, then creates tasks with clear owners—no context lost.

Turn prompts into Task briefs
Drop in a line like “Redesign the landing page for the new GTM campaign.” AI expands it into a full task description with:

• Deliverables
• KPIs and objectives
• Suggested collaborators
• Links to relevant Docs (if they exist)

Auto-organize tasks as you go
AI can file tasks into the right List, suggest smart tags like #urgent or #UX, and flag dependencies from the wording itself.

Draft content in context
Need a follow-up email, a meeting recap, or a status report? AI can generate it—directly inside the task, fully aware of your project’s progress.

Most AI tools help you write. AI helps you ship. That’s the difference!

Chat is also powered by AI, this enables you to summarize chats whether you are returning to office after a vacation or just don’t want to go through a long thread of conversation history.

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🔗 Integrations = Cross-tool execution without the chaos

A true AI agent doesn’t just live in your task list. It needs to connect across your tools, fetch data, and take action wherever work happens. That’s where ’s native integrations and open API make the difference.

Your AI agent can:

Schedule meetings via Google Calendar
Suggest times based on assignee availability, auto-create the event, and drop the link into or Slack.

Send updates in Slack or Microsoft Teams
Trigger alerts when milestones are hit, deadlines shift, or blockers are logged—tagging the right people with the right context.

Push changes to dev tools like Jira or GitHub
Automatically move tasks to QA, sync issue status, or comment on pull requests when tasks are completed in .

Attach files from Google Drive or Dropbox
Detect file mentions in comments, search cloud storage, and link the right asset directly to the task or Doc.

The result? Your agent stops being a siloed bot—and becomes a real team player.

🛠 Build your own AI agent (no dev required)

You don’t need a data scientist or a dev team to set up a powerful AI agent in . You already have everything you need: visual builders, automation logic, and prebuilt AI actions that work out of the box.

Get started in 3 steps:

1. Define your trigger
   Decide what will activate the agent—a task status change, a new form submission, a field update, or something else.
2. Add AI logic
   Layer in intelligence to summarize, classify, suggest checklists, or prioritize based on urgency or client type.
3. Set your outcome
   Automate what happens next: assign the task, notify someone, set a due date, or drop it into a sprint or folder.

Once it’s live, your AI agent is ready to work—without code, without training, and without slowing down your team.

The Future of Workflows Is Agentic—and It’s Already Here

RAG, MCP, and AI agents each serve powerful but distinct purposes in AI system design. While RAG helps ground outputs with real-time data and MCP brings long-term memory into interactions, it’s AI agents that represent the future—autonomous systems that plan, act, learn, and integrate across tools.

As the future trends in artificial intelligence continue to evolve, the fusion of generative AI with external systems and sequential decision making is reshaping how agents operate. Agents can incorporate external data and even run custom code to execute complex actions without being limited to templated workflows.

And with , you’re not just reading about the future—you’re building it.
Whether you’re creating self-operating workflows, launching AI-powered assistants, or scaling cross-functional teams, AI gives you the tools to centralize knowledge, automate execution, and enable intelligent decision-making—all in one place.

The result? Less busywork. More momentum. And workflows that run themselves.

Now that’s agentic productivity. Sign up with and explore AI agents on your own!

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