Context engineering + MCP = Super Agent ?

How Codiris builds products people actually want.

Hello,

This week, we’re diving into the game-changing synergy of Context Engineering and the Model Context Protocol (MCP) a combo that’s redefining what AI agents can do. Buckle up, because this is where generic chatbots evolve into super agents that deliver precise, scalable, and context-aware solutions.

You’ve probably heard about prompt engineering crafting clever instructions to get better AI outputs. But the real magic happens with context engineering, the art of curating the right information, tools, and memory for an AI to tackle complex tasks. Think of it as giving your AI a perfectly organized toolbox instead of a cluttered junk drawer. Now, pair that with the Model Context Protocol (MCP), a new open standard from Anthropic that standardizes how AI agents connect to external data and tools, and you’ve got a recipe for super agents AI systems that are dynamic, context-aware, and ready to scale.

Context engineering is about designing the entire mental world an AI operates in. It’s not just about asking, “Write a newsletter,” but providing the AI with:

Who: Your target audience (e.g., AI developers, enterprise leaders).
What: The task’s goal (e.g., inform and inspire about AI advancements).
How: Tone, style, and constraints (e.g., concise, professional, 500 words).
Why: The purpose (e.g., drive engagement with agentic AI concepts).

Without context, you get generic responses. Too little context, and answers are vague. Too much, and you risk hallucinations. The sweet spot? A carefully curated context that focuses the AI’s attention like a laser.

To understand context engineering, we must first expand our definition of "context." It isn't just the single prompt you send to an LLM. Think of it as everything the model sees before it generates a response.

Instructions / System Prompt: An initial set of instructions that define the behavior of the model during a conversation, can/should include examples, rules ….
User Prompt: Immediate task or question from the user.
State / History (short-term Memory): The current conversation, including user and model responses that have led to this moment.
Long-Term Memory: Persistent knowledge base, gathered across many prior conversations, containing learned user preferences, summaries of past projects, or facts it has been told to remember for future use.
Retrieved Information (RAG): External, up-to-date knowledge, relevant information from documents, databases, or APIs to answer specific questions.
Available Tools: Definitions of all the functions or built-in tools it can call (e.g., check_inventory, send_email).
Structured Output: Definitions on the format of the model's response, e.g. a JSON object.

Introduced by Anthropic in November 2024, MCP is like a USB-C port for AI agents. It standardizes how AI connects to external data sources (e.g., Google Drive, Slack, GitHub) and tools, replacing clunky custom integrations with a unified protocol. With MCP, agents can:

Pull real-time data from your CRM, codebase, or cloud storage.
Execute actions like sending emails or updating files.
Maintain secure, scalable connections across diverse systems.

MCP makes agents context-aware by enabling seamless access to the right information at the right time, without developers hardcoding every integration. Lot of companies are already using MCP to power intelligent workflows, from contract processing to dynamic web content generation.

When you combine context engineering’s deliberate curation with MCP’s standardized connectivity, you get super agents AI systems that:

Understand deeply: They access relevant data (e.g., your calendar, past emails) to make informed decisions.
Act dynamically: They use tools via MCP to perform tasks like scheduling meetings or generating reports.
Scale effortlessly: MCP’s universal protocol reduces integration overhead, while context engineering ensures consistency across users and tasks.

Imagine an AI assistant scheduling a meeting. A “cheap demo” agent might respond, “What time works?” A super agent, powered by context engineering and MCP, checks your calendar, pulls past emails for tone, and sends an invite: “Hey Jim, tomorrow’s packed, but Thursday AM is free. Invite sent!”

How to build your own super agent

Here’s a practical guide to leveraging context engineering and MCP to create an AI that feels like a superpowered teammate:

Define the Context:
- System Prompt: Set clear instructions (e.g., “Act as a project management assistant for tech startups”).
- User Input: Specify the task (e.g., “Draft a sprint planning email”).
- Memory: Include conversation history or user preferences.
- External Data: Use MCP to pull relevant info (e.g., Jira tickets, Slack messages).
Optimize the Context:
- Select: Choose only the most relevant data (e.g., recent project updates, not the entire Jira history).
- Compress: Summarize long documents to fit token limits.
- Isolate: Store sensitive data separately and fetch it via MCP only28f6-4b3d-4c8e-9f5b-3a7e1c2d5f6e only when needed.
Use MCP for Dynamic Tools:
- Connect your agent to tools like a calendar API or email client via MCP servers.
- Ensure tools return structured, digestible data (e.g., JSON-formatted calendar slots).
Iterate and Refine:
- Test outputs and refine context if responses are off-target.
- Use frameworks like LangGraph to manage context flow and maintain focus.

How Codiris uses context engineering

Codiris is an AI-native Product Development Environment (PDE) built to eliminate tool-switching and context loss while building software. Here’s how it leverages context engineering:

Unified Product Context: Every stage from brainstorming to deployment feeds into a single context memory. No more losing user insights between design, coding, and QA.
Agent-to-Agent Context Sharing: Specialized Codiris agents (UX, backend, QA, etc.) share a common memory, ensuring seamless collaboration and no duplication of effort.
Persistent Memory: Codiris tracks short-term (current tasks) and long-term (past features, design decisions) context across projects.
Intelligent Tool Integration: Codiris connects to Git, Figma, APIs, and databases. When coding, it automatically references designs, existing schemas, and previous iterations.
User-Centric Retrieval: It pulls user feedback, market research, and feature requests directly into the design and coding phase helping you build what people actually want.

Too much context can be a problem large prompts filled with irrelevant data confuse even the best AI models. Codiris avoids this by curating and filtering context intelligently:

Context Summarization: Instead of dumping all history, Codiris creates compact summaries of key decisions.
Dynamic Retrieval: It fetches only relevant data on-demand (e.g., past UX decisions for a specific feature).
Agent-Specific Context Windows: Each agent gets only what it needs to perform its task while maintaining a shared high-level memory.
Priority Layers: Active tasks and user requirements take precedence over archived history.
Iterative Reasoning: Codiris fetches more data only if needed reducing hallucination and improving accuracy.

The result? Sharper, more relevant AI outputs that stay aligned with your goals.

Most startups fail because they build something no one wants. Codiris fixes this by:

Embedding user research and feedback into every step of the product cycle.
Preserving the why behind each feature, so code and UX always match real needs.
Eliminating the productivity drain of switching between disconnected tools.

In short: One product engineer + Codiris = a full product squad. By combining context engineering with MCP, Codiris shows how Super Agents will shape not only software development but every industry.

The AI race is no longer about writing single prompts it’s about orchestrating context and intelligence to create systems that think, plan, and build alongside us. Codiris is proving that with the right approach, Super Agents aren’t a futuristic dream they’re here today.