Mastering AI Context Management: From Challenges to Lumi’s Model Context Protocol

‍

The AI Context Challenge

Modern AI systems, especially large language models (LLMs), are incredibly powerful but come with a key limitation – context. These models operate within a fixed “context window” of recent information and struggle when the necessary data isn’t readily at hand. Even the most advanced AI today remains largely isolated from live data, trapped behind silos unless we explicitly feed it the info it needs​. This limitation isn’t new. Historically, AI engineers have battled with getting relevant context to their models. Early chatbots would easily “forget” facts given just a few turns earlier. Knowledge bases and memory systems were bolted on as workarounds, but integrating them was cumbersome. Every new data source – a database, an API, or a document repository – often required a custom integration to make it accessible to the AI​. In short, context integration has long been a bottleneck in AI development.

One high-profile illustration of this challenge is the task of incorporating real-time external data. Consider a user asking an AI assistant: “What’s the picture of the day from NASA and why is it interesting?” An LLM that only knows up to its training cutoff date would have no clue – unless we give it the latest context from NASA. In practice, a solution might involve calling NASA’s public API for the Astronomy Picture of the Day (APOD) and feeding the result into the AI. NASA provides open APIs that return rich information; for example, a call to the APOD endpoint will return a JSON payload containing the image URL, title, and description of the day’s picture​. The AI then needs to integrate that data into its answer. Without such external context retrieval, the model would either hallucinate an answer or respond with a useless “I don’t have that information.”

Now multiply this scenario across countless domains – finance, healthcare, customer support, analytics – and you see a pattern. An AI is only as good as the information it has in context. If we want an enterprise chatbot to answer customer-specific questions, we must give it access to customer data. If we want a coding assistant to be aware of a project’s entire codebase, we must supply that code (or summaries of it) when asking questions. Simply training ever-larger models isn’t enough; they require relevant context at runtime to stay accurate and useful. Unfortunately, shoehorning in all that context has been non-trivial. Developers often resorted to brittle pipelines: e.g. a user query triggers some keyword logic or vector database lookup to fetch documents, which are then concatenated into the prompt for the LLM. This approach (known as retrieval-augmented generation) does improve relevance by injecting external knowledge​. However, it introduces new problems like choosing the right pieces of data to include (too little and the model lacks info, too much and you hit context limits or confuse the model).

The historical challenges around AI context can be summarized as follows:

‍

• Limited Memory: Until recently, models could only handle a few thousand tokens of context (a few pages of text). Important details often fell outside this window, causing models to forget or ignore them.
• Siloed Data: AI systems did not natively connect to external databases or APIs. Each integration (like connecting an AI to your CRM or to a third-party service) meant custom code and glue logic​.
• Complex Retrieval Logic: Developers had to build custom retrieval pipelines. For instance, a support chatbot might need to search a knowledge base for relevant articles to include in the prompt. Implementing and tuning this search-and-inject mechanism took significant effort, and it was easy to get wrong (returning irrelevant info or missing the key piece of data).
• Real-Time Information: Models trained on static data quickly go out-of-date. To get current info (stock prices, weather, news, today’s NASA image), external calls are mandatory. But orchestrating those calls and merging results into the AI’s response required careful planning.
• Hallucinations and Errors: Without the right context, AIs tend to guess. We’ve all seen AI answers that look plausible but are completely incorrect. Often that’s because the model lacked authoritative context and filled the gaps with its learned prior knowledge – sometimes with amusing or disastrous results. Ensuring the model has the right data in context is key to reducing these hallucinations.

‍

At Lumi, we experienced these pain points firsthand when building AI-driven analytics tools. We saw that an AI assistant for business intelligence needs access to business context – from data models to definitions of metrics and even tribal knowledge in the company. Simply dumping a schema definition or a few example rows into the prompt wasn’t enough. The AI needed a dynamic way to query for what it needed at runtime (e.g. “the latest sales numbers for this month”) and incorporate that into its answer. This realization shaped Lumi’s perspective on AI context management: to truly unlock AI’s value, you must treat context as a first-class citizen. It’s not an afterthought or a nice-to-have, but the backbone of any practical AI application. We believe that AI systems should seamlessly connect to the sources of truth – whether that’s a database, an API, a document repository, or another tool – and pull in the relevant context on demand. This philosophy set the stage for Lumi’s approach to solving the context challenge, which we’ll explore next.

‍

Introducing the Model Context Protocol (MCP)

‍

To address these challenges head-on, Lumi has embraced and extended a new industry standard called the Model Context Protocol (MCP). The Model Context Protocol is an open standard (originally proposed by Anthropic in late 2024) designed to make context integration plug-and-play​. Think of MCP like a USB-C port for AI applications​. Just as USB-C provides a standard way to connect devices to all sorts of peripherals, MCP provides a standardized way for AI models to connect to different data sources and tools. In simpler terms, MCP defines a uniform interface for feeding context to AI from any source. This is a game-changer for developers who have struggled with one-off integrations.

So, what does Lumi’s approach with MCP look like in practice? At a high level, Lumi uses MCP to bridge our AI models with a variety of enterprise data sources effortlessly. Under the hood, MCP follows a client-server architecture​:

‍

• MCP Server: A lightweight adapter that exposes a data source or service. This could be a wrapper around a database, an API, a cloud app (like Slack or GitHub), etc. The server defines what “tools” or actions are available (for example, a NASA API server might define a tool called get_apod(date) to fetch the Astronomy Picture of the Day for a given date).
• MCP Client: Typically the AI assistant’s side. The client (running alongside the AI model) knows how to discover available MCP servers and ask them for information or actions.
• MCP Host (AI model): The AI model (like Claude or GPT) that is now augmented with the ability to use tools via the MCP client. From the model’s perspective, it has a toolbox of actions it can invoke. It’s similar to how a plugin system works, but standardized.

When a user asks a question, Lumi’s AI can dynamically determine if it should call one of these external tools to get more context. Let’s revisit the NASA example, now with Lumi’s MCP integration:

‍

An AI using MCP to call NASA’s API

‍

‍Suppose a user asks, “Show me today’s NASA image and summarize the description.” Through MCP, our AI knows there is a tool available (provided by an MCP server) for NASA’s APOD. Instead of replying “I don’t have that info,” the AI can invoke the get_apod tool. The MCP server for NASA’s API receives this request, calls the actual NASA API, and returns the result (e.g. the title, image URL, and explanation text). The AI then uses that result to formulate a complete answer for the user. All of this happens within seconds, and from the user’s perspective the AI just magically knew the answer.

‍

To illustrate how straightforward this is for a developer, let’s look at a simplified code snippet. First, without MCP you might integrate NASA’s API like this:

import requests

# Without MCP: manually fetch NASA APOD data
API_KEY = "DEMO_KEY"  # NASA API demo key
date = "2025-03-07"
url = f"https://api.nasa.gov/planetary/apod?api_key={API_KEY}&date={date}"
response = requests.get(url)
data = response.json()

print(data["title"])
# e.g., "Planetary Nebula Abell 7"
print(data["explanation"][:100] + "...")
# e.g., "Very faint planetary nebula Abell 7 is about 1,800 light-years distant. It lies just south of Orion..."

‍‍


In the above sample code, we manually call the NASA APOD API for a given date and get back a JSON with details. We then extract the title and explanation. In a non-MCP world, a developer would then take data["title"] and data["explanation"] and concatenate them into the prompt for the LLM, maybe like: “NASA’s picture of the day is titled [Title]. Description: [Explanation].” and ask the model to summarize or answer the user’s question. This works, but it means our application logic has to handle calling the API and feeding the model. It’s a lot of plumbing that gets repeated for every new data source.

Now let’s see how MCP simplifies this. With Lumi’s approach, we would implement a small MCP server that wraps the NASA API. Pseudocode for an MCP server might look like:

# With MCP: define a NASA APOD tool in an MCP server
class NasaApodServer(MCPServer):
    def __init__(self):
        super().__init__(name="NASA_APOD")
        # Define the tool and its interface for the AI (name, description, params)
        self.define_tool(
            name="get_apod",
            description="Fetch NASA Astronomy Picture of the Day info for a given date",
            parameters={"date": "string (YYYY-MM-DD)"},
            handler=self.get_apod_handler
        )
    
    def get_apod_handler(self, date:str):
        # Actual implementation of the tool
        API_KEY = "DEMO_KEY"
        url = f"https://api.nasa.gov/planetary/apod?api_key={API_KEY}&date={date}"
        resp = requests.get(url).json()
        # Return a concise result for the AI (could be JSON or text)
        return {
            "title": resp.get("title"),
            "description": resp.get("explanation")
        }

# Start the MCP server (so it listens for requests from AI clients)
server = NasaApodServer()
server.run()

Meanwhile, on Lumi’s AI side, the MCP client registers that the “NASA_APOD” service is available and that it provides a get_apod tool. Now, when the user asks their question, the AI internally generates something like a function call: get_apod(date="2025-03-07"). The MCP client routes this to our NasaApodServer, which calls the API and returns the result. The LLM then gets back a snippet of JSON or text it can use. Finally, the AI produces an answer such as: “NASA’s picture of the day is Planetary Nebula Abell 7. It’s a faint planetary nebula about 1,800 light years away, captured in a deep telescopic image. In summary, the nebula is a spherical cloud of gas that’s the last breath of a dying sun-like star.” – which combines the fetched context with the model’s own explanatory power.

The beauty of MCP is that from the AI developer’s perspective, the integration is standardized. We didn’t have to custom-code how the AI parses the API response or worry about prompt formatting – the MCP framework handles the exchange in a structured way. In fact, tools like Cloudflare’s workers-mcp have emerged to further streamline this, letting you deploy an MCP server in a few lines of code​ . In the Cloudflare example, you can annotate a function (say sayHello(name)) with a JSDoc comment explaining its purpose, and the MCP machinery will expose it so that an AI like Claude can understand and call sayHello when needed​. This level of simplicity in defining tools means that developers can expose new data sources to AI rapidly, without having to constantly consult lengthy API docs or write glue code for parsing and formatting.

From Lumi’s standpoint, adopting MCP has had profound implications for cost, accuracy, scalability, and governance:

‍

• Cost Efficiency: By using MCP, we fetch only what the AI needs when it needs it. This prevents us from stuffing huge amounts of reference data into every prompt “just in case,” which would inflate token counts and API costs. For example, rather than always appending a 500-word company policy to the prompt, we can have an MCP tool that provides a specific policy excerpt on demand. Smaller, targeted context means cheaper and faster model inference.
• Improved Accuracy: When the AI can reliably pull factual data from authoritative sources (like a live database or a vetted API), its answers are more accurate. We’ve observed a significant reduction in hallucinations. If the answer requires a number or an up-to-date detail, the AI uses the tool and gets the ground-truth data instead of guessing. This also improves user trust – the AI can even cite the source of its information if needed.
• Scalability: In the pre-MCP world, adding support for a new integration (say connecting your AI to Salesforce, or to an ERP system) meant a lot of custom engineering. With MCP’s standardized interface, it’s far easier to plug in a new connector. In fact, the ecosystem is growing; Anthropic and others have shared pre-built MCP servers for Google Drive, Slack, GitHub, databases and more​ Lumi leverages this by quickly integrating clients’ systems via existing connectors or rapidly building new ones. Instead of reinventing the wheel each time, we build against the MCP standard and get a consistent, reliable connection. As the standard evolves, those improvements benefit all our integrations at once.
• Governance and Security: Enterprise customers care deeply about controlling what data an AI can access and tracking how it’s used. MCP provides a clear layer where access can be audited and managed. Because each MCP server is a gateway to a specific data source, it can enforce permissions (e.g., maybe the AI can fetch customer account data but not payment details). We can log every tool invocation – essentially a record that AI X accessed data Y at time Z. This is hugely beneficial for compliance and debugging. If an AI makes an unexpected decision, we can check the tool usage logs to see what it retrieved and when. In Lumi’s implementation, all data flowing through MCP can be monitored, and we can easily disable or tweak connectors without touching the core AI logic.

‍

From a developer experience perspective, MCP has transformed how we build AI features at Lumi. One of our engineers likened it to working with microservices: each data source is a service with a defined API (the MCP server), and the AI is an orchestrator calling those services. This decoupling means you can have different teams or vendors build MCP connectors for their systems, and as long as they conform to the protocol, the AI can use them. Early adopters in the industry, like the team at Block (Square), have praised this approach for making AI integration more collaborative and scalable across organizations​. The AI no longer exists in a vacuum – it becomes a connected part of the tech stack.

‍

Options For Immediate Context Optimization

‍

Not every enterprise is ready to deploy a full-fledged MCP integration overnight, and that’s okay. There are several immediate steps you can take to enhance your AI’s context management and get quick wins. Drawing from Lumi’s experience, here’s some free advice for improving AI context handling in your organization right now:

‍

1. Audit Your AI’s Current Context Workflow: Start by mapping out how your AI applications currently obtain context. What data are you feeding into your model prompts or API calls? Is it pulling from a static knowledge base, doing a database query, or relying solely on the user’s input? By auditing this, you may discover gaps (e.g., the AI doesn’t have access to up-to-date product info) or inefficiencies (e.g., every query attaches a huge document even when not needed). Engaging domain experts can help identify what crucial context the AI might be missing. For instance, your customer support bot might be forgetting to check the customer’s subscription tier when answering questions – a quick integration to include that detail can dramatically improve its responses.

2. Slim Down and Sharpen the Context You Provide: More data isn’t always better. Feeding extremely large contexts can lead to higher costs and even confuse the model with irrelevant info. See if you can streamline the prompts. Perhaps you have old examples or verbose instructions in the prompt that could be trimmed. Or maybe the AI only needs a summary of a policy rather than the entire 10-page policy text each time. By improving the signal-to-noise ratio of the context, you make it easier for the model to focus on relevant details (and you save on processing time). One tip is to use embedding-based search on your documents: instead of inserting everything, compute vector embeddings of your knowledge base and retrieve the top-N relevant chunks for each query; this way, the AI only sees the pertinent facts. If you’re already doing this, double-check the relevance – ensure the retrieval step isn’t returning spurious text that might mislead the model. It’s worth tuning the similarity threshold or using metadata filters to hone what gets pulled.

3. Introduce a Basic Retrieval Augmentation if You Haven’t: If your AI system is completely static (no dynamic info retrieval), consider adding a simple retrieval mechanism. This doesn’t require deep architectural changes. For example, you could plug in a FAQ lookup: when a question comes in, first check if it matches a known FAQ and if so, provide that context to the model. Even something as straightforward as keyword search on a documentation corpus that feeds the top result into the prompt can boost accuracy significantly. Many teams start with a lightweight implementation like this and see immediate improvement in factual correctness. Just be mindful to verify the retrieved text – you don’t want to accidentally feed the model outdated or irrelevant info. A quick review process or human-in-the-loop mechanism for new or contested queries can be helpful.

4. Future-Proof Your Integrations: When adding any new context source or integration, design it in a way that it can be extended later. You might not implement MCP right away, but you can move in that direction by separating your “context fetching” code from your core logic. For instance, have a module or service whose sole job is to interface with external data and produce context for the AI. Then call that module from your main app. This separation means that down the line, swapping in a more standardized approach (like MCP or another tool framework) will be easier – you won’t have to untangle a mess of hardcoded logic from your business code. Also, try to use configuration or metadata to drive what context is used. Perhaps maintain a simple config file that lists sources to query for each type of AI request. This way, adding a new source becomes a config change, not a code change. Such foresight will save you time as your AI needs grow.

5. Leverage Existing Tools and Platforms: You don’t have to build everything from scratch. There are emerging platforms, like semantic search services and vector databases (Pinecone, Weaviate, etc.), and libraries like LangChain or Semantic Kernel, that can handle a lot of the heavy lifting for context retrieval and integration. For example, Microsoft’s Semantic Kernel now supports integrating MCP tools via function calls​. If you experiment with these, you’ll not only improve your system today but also position yourself to adopt standards like MCP more quickly tomorrow. It’s a way of future-proofing – by aligning with where the industry is headed, you won’t be caught flat-footed when new capabilities roll out.

6. Incorporate Human Feedback and Oversight: This might not sound like a context issue at first, but it’s crucial. Monitor your AI’s outputs and let users flag when an answer is wrong or lacking context. Each failure is a learning opportunity to identify what piece of context could have prevented it. Maybe a user asks a question and the AI gives a generic answer, but the user was expecting something specific to their account. That’s a sign the AI might need access to account data. Having a feedback loop (even as simple as a thumbs-up/down on answers, or reviewing chat logs) can guide your next integration efforts. This ensures you focus on high-impact context gaps first. Lumi, for example, often pilots a new AI feature internally with employees and watches where the AI stumbles – 90% of the time, the fix is “let’s feed it this missing info or teach it how to look this up” rather than altering the model itself.

‍

While implementing the above, always keep governance in mind. As you enhance context, you may be piping more data into your AI, some of which could be sensitive. Put in place access controls. If you give an AI access to an internal database, ensure it can only retrieve what it’s supposed to. Simple measures like role-based permissions or query whitelisting can prevent accidents. Also, log everything when possible. You want an audit trail of what the AI accessed and did – this will be invaluable for debugging and for trust. Enterprises often already have data governance practices; extend those to your AI context pipeline as well.

In summary, optimize what you have: trim the fat, fetch what’s missing, and structure your system in a modular way. These steps will yield immediate improvements – your AI will be more efficient and accurate. And importantly, you’ll be laying the groundwork so that adopting a more powerful solution like Lumi’s MCP will be a smooth transition rather than a daunting overhaul. Essentially, you’re moving your organization up the “AI maturity curve” – from ad-hoc prompt engineering towards a robust, context-aware AI strategy.

‍

AI Context Management Trends

‍

The pace of advancement in AI context management is breathtaking. What was a cutting-edge idea a year ago can become standard practice the next. Looking ahead, several key trends are poised to shape how we handle context in AI systems. Here’s a glimpse of the future and how Lumi is positioning itself (and its customers) to stay ahead:

Extended Context Windows (AI Memory Expansion): One obvious trend is that the raw context capacity of models is growing. Anthropic’s Claude model made headlines by expanding to a ~100,000 token context window (around 75,000 words)​, and other AI providers are racing in the same direction. This means future models will be able to digest entire books, codebases, or years of chat history in one go. For example, you could feed a year’s worth of transaction data to the model and directly ask for trends, without needing an external database query. Anthropic demonstrated Claude reading The Great Gatsby (72K tokens) in one shot and detecting a subtle edit in seconds​ – a feat that would have been impossible with previous limits. And just recently, we’ve seen GPT-4 variants with 128K token windows being discussed. It’s not hard to imagine million-token contexts on the horizon.

However, bigger context alone isn’t a silver bullet. There are practical limits: feeding 100K tokens costs a lot and still requires you to have those tokens (i.e., you need the data upfront). What if the info you need isn’t already in your document? That’s where retrieval remains crucial. Large context models will work best in tandem with smart retrieval – you bring as much as you reasonably can into the prompt, and use retrieval to cover anything beyond that. Interestingly, Anthropic noted that for certain complex questions, giving Claude all the raw text might work better than a traditional vector search approach​. This suggests a hybrid future: we’ll use extended context to handle broad, knowledge-synthesis tasks and use retrieval for pinpoint facts or dynamic updates. Lumi is preparing for this by ensuring our systems can take advantage of larger context windows as they become available. Our platform is model-agnostic, so when a 200K-token or 1M-token model comes out, we can swap it in and immediately let customers load more data into each query. Imagine an AI that can take an entire SAP user manual plus your company’s process documentation in one prompt – we’re not far off from that.

Smarter Context Retrieval (Intelligent Augmentation): Beyond brute-force context size, the methods for retrieving and injecting context are getting smarter. The basic “top-N similar chunks” retrieval will evolve into more nuanced strategies. We’re seeing research and tools that do multi-hop retrieval, where the AI might first ask a clarifying question or retrieve context in stages (like first find a relevant document, then within that find a specific passage). There’s also interest in context deliberation – essentially an AI agent that double-checks the context it gathered and cleans it up before use​. At Lumi, our research is exploring a novel approach we call the Signal Curator—an intelligent filtering layer that refines information before it reaches the core AI model. Similar to how a skilled editor distills raw research into a concise, high-impact brief, the Signal Curator ensures that only the most relevant and valuable information is passed forward.

Rather than overwhelming the model with excessive data, the Signal Curator sifts through retrieved content, prioritizing quality over quantity. It selects the most meaningful snippets, restructures or summarizes them if necessary, and optimizes context delivery. This reduces token consumption, enhances efficiency, and improves response precision, making AI-powered workflows more streamlined and cost-effective. Another key trend is personalization of context – AI systems learning which sources or types of data yield the best answers for a given user or scenario, and prioritizing those. For example, an AI assistant that observes that a certain internal wiki page has been very useful for finance questions might start to always check that page first when finance queries come up. This kind of learned retrieval policy will make context injection more efficient over time as the AI “figures out” where the gold nuggets are.

Improved Embeddings and Indexes: On the technical front, the embeddings that underpin similarity search are improving. Newer embedding models capture domain-specific nuances better, meaning the AI can more accurately fetch the information you intended. We also see multi-modal embeddings emerging – imagine a single system that can index text, code, images, even audio, and find relationships across them. OpenAI, Meta, and others are working on embeddings that are not just higher-dimensional, but also smarter in understanding context (like knowing that “Mars” in a query might relate strongly to “Red Planet” in a document even if the words differ). For companies, this means their knowledge bases become more searchable and connected for AI usage. We might even move beyond pure vector similarity to hybrid models that consider symbolic relationships (like knowledge graphs) combined with embeddings. In practice, an AI might soon be able to do something like: “find me the section of the engineering manual where the torque value was mentioned in context of part ABC” and get a reliable result, because it can combine textual similarity with the structured knowledge that part ABC is related to a certain subsystem. Lumi is watching these developments closely and planning to integrate improved embedding techniques into our context pipeline – so our customers benefit from more accurate retrieval without needing to constantly fine-tune things manually.

Standardization and Interoperability: The introduction of MCP is part of a larger movement towards standardizing how AI systems interact with tools and data. OpenAI’s plugins, while a bit different, share a similar goal of tool integration (though they were initially proprietary). We anticipate that in the near future, there will be convergence or bridges between these ecosystems. It’s conceivable that a single AI assistant could adhere to multiple standards – for instance, an AI that can use MCP for certain enterprise tools and also understand OpenAI plugin APIs, etc. Efforts like MCP have an open governance approach, so we expect more industry players to adopt it. In fact, big names (Block, Replit, Sourcegraph, and many others) have already been experimenting with MCP​. This trend means that developers can reuse context integration solutions across different AI platforms. At Lumi, flexibility is at the core of our approach. We design and implement enterprise AI architectures that support standardized protocols (like MCP), ensuring seamless integration with any model or service our clients prefer. Whether a client chooses Anthropic’s Claude or a custom-built model, we make sure their AI can interface effortlessly with their existing data and systems. Our focus is on interoperability, eliminating vendor lock-in and the need for cumbersome one-off adapters. Your AI should work with your data—wherever it lives—without limitations. By embracing open standards, we future-proof AI deployments, enabling businesses to adapt and scale with confidence.

‍Enhanced Memory and Continual Learning: Another aspect of context is long-term memory – not just what the AI can handle in one conversation, but what it can remember across sessions. We foresee AI systems getting better at building and maintaining a knowledge base of what they’ve seen and learned (with appropriate privacy controls). For example, after resolving an issue for a user, an AI helpdesk agent might store a summary of that solution. Later, if a similar issue arises, it recalls that past case as additional context. This is different from the static training; it’s more of a dynamic, ongoing learning that augments context. Techniques like fine-tuning are too heavyweight for this on-the-fly, but approaches using embeddings or specialized memory models could fill the gap. Startups and research projects are already tackling “memory architectures” for LLMs that let them retrieve relevant past interactions efficiently. We expect these to mature, allowing AI to have a sort of organizational memory. Lumi is excited about this because in domains like analytics, context isn’t just external data – it’s also the conversation history, the hypotheses tried, the feedback given. If the AI can remember that you already looked at revenue last week and now you’re exploring costs, it can avoid rehashing old ground and instead provide deeper insights by building on that prior context.

Lumi’s Ongoing Innovations: In light of these trends, Lumi is continuously innovating our context management capabilities. We view MCP as a foundational layer – a highway for data to travel into the AI’s brain. On top of that, we’re adding “smart traffic control” in the form of context agents and dynamic retrieval strategies. We are also investing in tooling to make it easier for our customers to onboard their data into our their own context ecosystems. For instance, we’re building connectors (using MCP and other APIs) for a wide array of enterprise systems, from SQL databases to SaaS applications, so that a new Lumi customer can flip a switch and grant their AI access to their Enterprise resources.

In summary, the future of AI context management is one where AI systems have more memory, smarter ways to fetch info, and more standardized means to plug into the world. We’ll see AIs that feel increasingly “omniscient” – not because they magically contain all knowledge, but because they’re adept at finding and pulling what they need from the right places at the right time. Lumi’s mission is to ensure our clients ride this wave confidently. That means adopting the best of these advancements (like MCP and beyond) and layering on our domain expertise in analytics to deliver AI solutions that don’t just generate answers, but generate relevant, informed, and context-rich answers. The bottom line for a senior developer: by staying ahead of these trends and working with partners like Lumi who are at the forefront of context management, you can build AI applications that are robust, scalable, and ready for the future – where the only limit to an AI’s knowledge is no longer its training data, but simply how creatively it can retrieve and leverage the vast information available.

‍