Blog

Many clients are experimenting and requesting AI agents that query their databases. "Just ask questions in plain English and get instant insights!", sounds simple enough.SQL is language and LLMs speak if fluently. However, the truth is making agents reliably talk to your production data is far more complex than the demos suggest.<h2>The Three Hidden Challenges</h2>Simply put, here are three fundamental issues that make "talk to your data" much harder than it appears. Especially when you're dealing with real business systems, not demo databases.<h3>1. The Performance Trap: One Query, Many Paths</h3>Here's how database queries work in practical terms: there's usually more than one way to get the same answer. In a small demo database, who cares? But in your production environment with millions of records, one approach returns results in milliseconds while another locks up your system for minutes.When you unleash an autonomous AI agent on your database, you're essentially rolling the dice. Will it choose the efficient path with proper indexing? Or will it construct a query that sends your database administrator into panic mode?For example, imagine querying customer orders from the last month. The agent could:<ul><li>Use the indexed date field (fast)</li><li>Scan every single order and filter by date (disaster)</li><li>Join multiple tables unnecessarily (somewhere in between)</li></ul>Without careful engineering, you have no guarantee which approach your agent will take – today, tomorrow, or when the CEO is waiting for that critical report.<h3>2. The Variance Problem: When Consistency Matters</h3>Let's be honest about how businesses actually use data. You're not asking unique, creative questions every day. Most data queries are repetitive – and that's by design.Think about your typical reporting workflow:<ul><li>3-5 core tables you query regularly</li><li>3-5 standard queries for each table</li><li>These 9-25 queries probably cover 90% of your daily needs</li></ul>Why does this matter? Because these queries feed into dashboards, reports, and downstream processes. Your finance team needs the monthly revenue calculated the same way every time. Your inventory system depends on consistent stock counts. You probably have a definition of a "active account" or "paying user" that needs to be calulated the same way every time.But AI agents thrive on creativity. Ask "show me last month's sales" twice, and you might get two different interpretations. Was that calendar month or last 30 days? Gross or net? Including pending transactions or only completed? By category or total?What seems like helpful flexibility becomes a liability when you need reliable, reproducible results.<h3>3. The Translation Challenge: Your Database Speaks Its Own Language</h3>This is the killer: the more complex and specialized your database, the worse AI agents perform on it.Generic LLMs are trained on publicly available patterns. They understand common database structures and standard business terms. But your production database? It's full of company-specific logic that would baffle any outsider.What does an AI agent do when:<ul><li>The field "STAT_CD" having 'K' as the second character means an order has shipped?</li><li>"Customer Type 3" actually means enterprise clients (for historical reasons nobody remembers)?</li><li>The "deleted" flag being 0 means active, but only in certain tables?</li></ul>Your database is essentially speaking a dialect that evolved over years of business decisions, quick fixes, and "we'll document it later" moments. An LLM trained on generic data has zero chance of understanding these nuances without extensive preparation.<h2>The Reality: Your data investments pay out</h2>Here's what this means for your business: implementing "talk to your data" isn't just dropping in an AI agent and calling it done.You still need:Performance optimization – Guiding agents toward efficient query patterns Template engineering – Creating reliable patterns for your common queries Semantic mapping – Teaching the AI your specific business logic and terminology, with data catalogs and other best practice patterns Validation layers – Ensuring consistency in critical reporting scenariosThe promise of natural language data access is real, but the path requires the same thoughtful data engineering we've always needed, now combined with agentic AI expertise.<h2>Making It Work in Practice</h2>Companies need to approach this challenge with eyes wide open. The winners aren't trying to skip on the data work overnight. They're strategically deploying AI agents where they add value while maintaining control where consistency matters. They are building the necessary data layers, handbooks and dictionaries for the agents to utilize the data. You don't want to build black boxes or externalize your ontologies.<h2>The Path Forward</h2>The direction is clear: AI agents will revolutionize how we interact with data. But the journey requires more than just plugging in an LLM and hoping for the best.Success comes from recognizing that your production data environment is complex, specialized, and mission-critical. It demands the same rigor in implementation that you'd apply to any core business system, plus new expertise in making AI agents work reliably within those constraints.Ready to implement AI agents that actually understand your data? We've learned these lessons the hard way, so you don't have to. <a data-mce-href="https://destrain.ai/contact" href="https://destrain.ai/contact">Contact us</a> to discuss how to make natural language data access work in your real production environment.--- Jonas Pomoell is the founder and lead AI business consultant at Des Train AI Oy, specializing in deploying transformative generative AI solutions. With deep expertise in AI-driven business automation and data systems, Jonas helps companies navigate the complexities of implementing AI agents in production environments.

Common pitfalls of "Talk to your data"

When I'm talking about AI agents, I rarely talk about replacing people. Instead I turn the conversation into how AI can complement the employees in their every day work.The more autonomous AI becomes, the more critical human expertise becomes. Simply put, AI agents are brilliant executors, but they need expert direction to deliver real business value. This is especially true when talking about <a data-mce-href="https://destrain.ai/blog/what-is-an-ai-agent--the-definition" href="https://destrain.ai/blog/what-is-an-ai-agent--the-definition">real AI agents, not automated workflows</a>.<h2>Autonomous but not independent</h2>Yes, AI agents can do a lot of tasks. They can write reports, send emails, analyze and input data - even make plans and decisions. But the autonomity only drives value when complemented with experience and vision, rules which determine the success: the quality of human understanding guiding them.An AI agent without domain expertise is like an orchestra without a conductor. Full of potential, but without someone to shape the performance. It might play in tune for a while, but sooner or later it will lose direction. The expert brings the strategic insight, specialized understanding, and attention to detail that turn scattered notes into a symphony.AI agents operate autonomously, yet their independence ends where context begins. They move on their own, but only within the boundaries of human insight and strategic intent. And remember, that if they're not delivering these values, <a data-mce-href="https://destrain.ai/blog/almost-everything-called-an--ai-agent--isn-t" href="https://destrain.ai/blog/almost-everything-called-an--ai-agent--isn-t">they might not be agents at all</a>.<h2>Why Experts Become More Valuable</h2>AI agents excel at execution, but they need three things only humans provide:Context and judgment. AI can process information, but understanding which information matters in your specific business context? That requires experience. Your industry knowledge, customer insights, and understanding of nuance. Those things transform AI from a tool into a strategic advantage.Quality control and direction. Every AI output needs expert validation. Not only because AI makes mistakes, but because business decisions require human judgment about risk, brand voice, and strategic alignment. The expert eye catches what matters and the envetual mistakes too, which are essential of LLMs to provide the content.Creative problem-solving. AI follows patterns, it formulates what has already been created. Experts break them. When your business needs innovation, competitive differentiation, or solutions to unprecedented challenges, that's where human creativity becomes irreplaceable. After all, humans have immensly wider context windows.<h2>What This Means for Your Business</h2>A recent Wharton study shows that shows that AI implementation winners, those that have the most measured impact, are using it FOR THE BORING STUFF. That means the backoffice tools, the repetative things in everyday work and bottlenecks that used to make some processes impossible. Doing more with the same effort and tools. However, what they're not doing is running after the lastest shiny big thing of the month.This has been evident already for a while. Already a year ago in Grand One AI Day in Helsinki 7.11.2024, Päivi Arffman-Koskinen, a Business strategist from Omnicom Media Group, noted in their presentation that they do not see decrease in campaing budgets when using AI. Instead they see 10-25 % increase in effectivenes of the campaing.<h2>Your Experts, Amplified</h2>At Des Train AI, we're experiencing firsthand how this partnership transforms businesses. We don't implement AI to reduce headcount, we deploy it to make your existing experts exponentially more effective. We want to study your processes and create narrow, effective and to the point solutions fast to gain the advantage.Use our expertice in Python, AI agent frameworks, LLM models, MCP and the architectural landscape and help you transform your business.The most successful AI implementations we've seen share one characteristic: they're led by companies that value their human expertise and use AI to supercharge it. Allow them to execute at unprecedented speed and scale while maintaining their unique insights and judgment.Ready to see how AI agents can amplify your team's expertise? <a data-mce-href="https://destrain.ai/contact" href="https://destrain.ai/contact">Reserve 30 mins from our calendar</a> to and we'll take a quick overview of your business together for free.-- Jonas Pomoell is the founder and lead AI business consultant at Des Train AI Oy, specializing in deploying transformative generative AI solutions. With deep expertise in AI-driven business transformation, Jonas helps companies unlock productivity and growth through advanced, practical AI agent implementations. (1) A business journal from the Wharton School of the University of Pennsylvania Accountable Acceleration: Gen AI Fast-Tracks Into the Enterprise, 12.11.2025, <a data-mce-href="https://knowledge.wharton.upenn.edu/special-report/2025-ai-adoption-report/" href="https://knowledge.wharton.upenn.edu/special-report/2025-ai-adoption-report/" target="_blank" rel="noopener">https://knowledge.wharton.upenn.edu/special-report/2025-ai-adoption-report/</a>

AI Agents Need Experts – Why People Matter More Than Ever

According to Gartner, 95% of what's being promoted as "AI agents" aren't actually agents at all.We're experiencing what Jordan Wilson from Everyday AI podcast brilliantly calls the "overagentification of everything." (E582)If you're not totally sure about what makes an AI Agent read our previous post <a data-mce-href="https://destrain.ai/blog/what-is-an-ai-agent--the-definition" href="https://destrain.ai/blog/what-is-an-ai-agent--the-definition">What Is an AI Agent? The Definition</a> <h2>The great mislabeling</h2>Simply put, companies are slapping the word "agent" on anything that even touches an LLM. As Wilson points out: "Much of the market hype misuses the term 'agent'. Anything with LLM isn't agent, agentic or even meaningful AI."But why does this matter to your business?Because this confusion is creating a massive gap between what businesses think they're buying and what they're actually getting.<h2>What people are asking for</h2>Here's a fascinating insight from Daniel Whitenack, CEO of Prediction, from the Practical AI podcast (E352): "Every time I've actually asked people about what they want with their AI agent, it turns out what they want is either RAG chatbot or automated workflow."<blockquote>If I had asked people what they wanted, they would have said faster horses.</blockquote>Think about that. Many businesses can't even imagine yet what agents are capable of. They're missing out on these dynamic assistants and turning instead to incremental upgrades: slightly smarter chatbots accessing company knowledge or predefined automated workflows with a sprinkle of increased capabilities compared to what was done 10 years ago already.They are valuable tools. They improve efficiency. They save time. A little bit.Agent's transform whole processes.<h2>The hidden cost of confusion</h2>This mislabeling isn't harmless marketing fluff. When companies label their LLM wrappers "agents" their customers think that "this is it". At the same time they dillute the real possibilities of the actual dynamic agentic shift. They become disillusioned with AI's actual potential. They fall behind competitors who understand the difference.What does this mean for your business? You might be settling for a chatbot when you could have a digital colleague. You might be buying into the 95% when you should be looking for the 5%.<h2>Why We're Writing This</h2>At Des Train AI, it's crucial that our customers know exactly what they're buying. We've had directors from large companies come to us stating that "Agents are nothing, they will never work". That smells like something a person disappointed with something sold to them as "agent". We can show they work, I'm running multiple agents as we speak.When we say "AI agent," we mean systems that dynamically figure out how to solve problems using the existing tools and complementing the persons doing the work. Not because it sounds impressive, but because that's the technology that actually transforms businesses. Setting the right expectations upfront means you get the transformation you're investing in, not another disappointment dressed up in AI buzzwords.<h2>How to Spot the Real Thing</h2>Here's your quick test. Ask your vendor: "Does this system decide HOW to accomplish tasks, or does it follow predetermined steps?"If they start talking about workflows, templates, or configurations you're probably looking at the 95%, not the 5%.Real agents don't need you to spell out every step. They figure out how to achieve the outcome you want. That's the transformative difference.<h2>The Path Forward</h2>Our mission is to complement people’s work with AI agents that help amplify the businesses they build.Why? Because when businesses understand what real AI agents can do: dynamically solving problems, taking ownership of processes, adapting their approach based on context, it changes everything.The technology for genuine AI agents exists today. One of the challenges is cutting through the "overagentification" to find it.Ready to join the 5% using real AI agents? <a data-mce-href="https://destrain.ai/contact" href="https://destrain.ai/contact">Contact</a> us to see the difference.

Almost Everything Called an "AI Agent" Isn't

Open book with turning pages. Textbook in hard cover on table. Education and studying concept. Blue grey background with copy space for text

An AI agent is a system where AI dynamically directs its own processes and tool usage, maintaining control over how it accomplishes tasks.Anthropic's definition above captures it pretty well. And this is a question I get a lot.That word "dynamically" is everything. It's the difference between following a recipe and being a chef. It separates the agent from chatbots and copilots by enabling autonomy and action.<h2>The Core Difference </h2>Not an Agent: You ask a chatbot to summarize the quarterly sales and it prints them to your screen. When you ask it to send it to your collague, it apologises for not being able to do that.Not an Agent: You tell a agentic process the steps it needs to take in order to analyze quarterly sales data. It fetches them according to your instructions, does the defined analysis and sends them where you have defined. You ask it to compare it with previous quarter and it freezes.Real Agent: You ask an AI agent to analyze sales data. It decides which data sources to examine, what timeframes matter, which metrics to compare, identifies anomalies you didn't ask about, and determines the best way to present its findings. It asks you for more detailed information and digs deeper into insights that interest you. When you see something it missed, you ask it to improve and it will.<blockquote>You tell it what you want to achieve, not how to achieve it.</blockquote>One needs instructions. The other figures it out.One is a one trick pony. The other solves varying problems using varying tools and data sources.One gives out what it wants. The other complements the user's expertise.That's it. That's what makes something an AI agent. You tell it what you want to achieve, not how to achieve it. (You need to give it enough guidance of course. And the tools, you need to hand in the tools. It's never that simple.)<h2>What This Looks Like in Practice </h2>Think of it like asking someone to get coffee. You don't need to say "walk to the kitchen, grab a mug from the cupboard, pour coffee in the mug..." You just say "bring me some coffee" and they figure out the steps. Add "two sugars", and they disgruntly return to the kitchen, but they know what it means.That's exactly how AI agents work. When you tell an agent "analyse the status of this shipment," you don't spell out every step. The agent reasons what is an shipment, where to get it's data, what checks are relevant and what needs to be done for different statuses. It might ask you clarifying questions or need instructions to improve the end result - but that's why you're there, you're the expert afterall. You're not the expert in reading a spreadsheet. Instead your an expert on understanding what is relevant of the information on the spreadsheet. Agents put the user's expertice is in the focus.What does this mean for your business? Increasingly flexible process supporting tools complementing employees exprtise. Using the same tools, spreadsheets, instructions and database as always. Internal and external data sources and tools. You're not programming workflows anymore. You're delegating outcomes.<h2>The Outcome</h2>Software that needs instructions saves time. Software that figures things out supercharges productivity.At Des Train AI, we focus on genuine AI agents for real business processes. The kind that dynamically control their own processes to achieve your goals. The kind that actually figure things out. Using your existing tools and infrastructure. Complementing your team's expertise.Is your business ready for AI that doesn't just assist but actually does?Ready to see what real AI agents can accomplish? <a data-mce-href="https://destrain.ai/contact" href="https://destrain.ai/contact">Contact us</a> to explore the possibilities.---Jonas Pomoell is the founder and AI business consultant at Des Train AI Oy, specializing in deploying transformative generative AI solutions. With deep expertise in AI-driven business automation, Jonas helps companies unlock productivity and growth through advanced, practical AI agent implementations. &nbsp;

What Is an AI Agent? The Definition

AI agents are revolutionizing busines processes and supercharging teams in their daily work. They don't only assist but actually get things done by reasoning and performing actions using the same everyday tools people are using in their work. For this they need the USB-C of integrations, the Model Context Protocol or MCP. [Read more on that from our previous blog on <a data-mce-href="https://destrain.ai/blog/how-do-ai-agents-actually-do-things" href="https://destrain.ai/blog/how-do-ai-agents-actually-do-things" title="How do AI Agents actually do things?" data-mce-selected="inline-boundary">How do AI Agents actually do things?]﻿</a>This Model Context Protocol revolution is changing everything about business tool integration. Tools that were never on your "integration radar" are suddenly becoming critical for AI agent workflows. Error tracking systems, project management platforms, communication tools and services you've always accessed manually are now expected to work seamlessly with AI agents.But here's what might surprise businesses starting to deploy AI agents: many modern, everyday tools still lack the comprehensive programmatic access that agents prefer. No API. No CLI. No filesystem access. And certainly no MCP integration. It's not a blocker but a speed bump.This isn't just about what tools you use anymore. It's about a fundamental shift and it's happening on two fronts:<ol><li>are your everyday tools ready for the MCP world</li><li>are the services you provide prepared for customers who expect MCP compatibility?</li></ol><h2>The Two-Sided MCP Reality</h2>This transformation is hitting businesses from both directions:As a tool consumer: Are your everyday SaaS business tools easily integratable with AI agents? Can your agents actually work with your error tracking, project management, analytics, and communication platforms? Or will you need complex automation workarounds for tools that should offer direct access?As a service provider: Are your services ready for customers who expect MCP integration? When your clients start deploying AI agents, will your platform be the one that "just works" or the one that requires expensive workarounds?The businesses recognizing both sides of this shift are the ones preparing for competitive advantage.<h2>The Great Tool Migration</h2>Take Google Firebase Crashlytics – used to manage millions of mobile apps daily, yet lacking API and CLI access for the basic operations that AI agents need to perform. And that is just one of the tools that feel thoroughly modern still operate with limited programmatic access. This pattern might be more common than many businesses initially realize.This is creating rapid market pressure, especially for tools where switching costs are low and alternatives exist.We're entering a period where integration capability will drive tool selection like never before. In SaaS categories where options are plentyful and switching costs are reasonable – error tracking, project management, analytics platforms, communication tools – the pressure to offer proper programmatic access is intensifying rapidly.Why? Because when your competitor's AI agents can seamlessly work with their entire tool stack while yours require manual intervention or complex automation, the productivity gap becomes unmistakable.At Des Train AI, we're expecting this pattern to accelerate. Companies evaluating new tools now ask about MCP compatibility upfront. Existing tools without proper API access are getting replaced, especially in categories where migration is straightforward.<h2>If switching is easy it should be done</h2>Everything can be integrated with AI agents – that's not the question. The question is efficiency and competitive timing.When switching costs are high (legacy enterprise systems, specialized industry software, deeply embedded workflows), building computer use automation for MCP use makes perfect sense. These integrations are worth the complexity.But when switching costs are low? When modern alternatives with native MCP support exist? When the tool is used frequently by agents? The calculation changes completely.This is should be rapid change in easily-switchable tool categories. And the tools that don't adapt quickly are losing customers to competitors who understand the new integration reality.And especially if you provide any tools, services or interfaces online including portals and websites - even for consumers. For a service provide&nbsp;the shift represents both risk and massive opportunity.The risk: Your internal processes, customers and partners will start expecting AI agent compatibility. If your platform requires complex workarounds while competitors offer direct integration, you'll lose to "the one that just works with our agents."The opportunity: Being MCP-ready positions you as the forward-thinking choice. When prospects are evaluating tools for their AI-enabled workflows, comprehensive integration support becomes a decisive factor.Companies doing their MCP implementation now will have competitive advantages over those waiting to see how the market develops.<h2>The change is silent</h2>This isn't a distant future consideration. AI agents are being deployed today, and their integration requirements are driving tool selection decisions right now.The question isn't whether MCP compatibility will matter. It's whether you make the right choices right now, both internally and with the broad spectrum of services you provide externally.The AI agent revolution is incremental and is happening within the everyday business processes. There will be no press releases of the agent deployments from your competitors. You'll know when you see them pass you due to their increased efficiency and quality it's already too late. But what if you could be the one on the driver's seat?Ready to evaluate both sides of your MCP readiness? The companies moving fastest are auditing their tool integration gaps while simultaneously planning their service integration capabilities. <a data-mce-href="https://destrain.ai/contact" href="https://destrain.ai/contact" title="Contact us">Contact us</a> to develop a strategy that addresses both your tool consumption and service provision in the MCP-enabled future.Interested to discuss more? <a data-mce-href="https://destrain.ai/contact" href="https://destrain.ai/contact" title="Contact">Book a meeting!</a>--- Jonas Pomoell is the founder and lead AI business consultant at Des Train AI Oy, specializing in deploying transformative generative AI solutions. With deep expertise in Model Context Protocol implementation and business tool integration strategy, Jonas helps companies navigate both the consumption and provision sides of the AI agent integration revolution.[The cover image taken 19.8.2025, real life questionnaire for paying users from widely used tranlsation and localization platform <a data-mce-href="https://phrase.com/" href="https://phrase.com/" title="Phrase">Phrase</a>]

Your tools need AI access. Your customers expect it too.

Business people analyze data and big data technology Two businessmen calculate business analysis and display images of complex data sets on a computer on the screen. artificial intelligence

AI agents are here – but how do they actually do things?Everyone’s talking about AI agents. But here's the real question: How do they actually access information and take action?Businesses today rely on countless digital tools—from calendars and email platforms to customer management systems and e-commerce applications. Typically, integrating new AI capabilities into these tools involves significant technical effort, ongoing updates, and substantial maintenance.But what if AI agents could seamlessly interact with these existing tools, without continuous updates or complex integrations?Enter the Model Context Protocol (MCP). Think of MCP as a lightweight, intelligent layer placed neatly on top of the systems your business already uses. Rather than recreating your current infrastructure, MCP enables AI agents to directly and effortlessly utilize your existing tools.<h2>Here's how it works in practical terms:</h2>Flexible Inputs: If an Agent can imagine it, the integration layer can consume it. Instead of making integrations that open up specific predefined reports from the reporting database, why not run reporting queries that the agent formulates for you based on your query. “Hey Agent, can you visualize last month's first-time purchaser’s ticket value” and continue with “Ok, now cross-reference those with our marketing campaign results”. Agent formulates the queries and chews up the results for you. But it’s not limited to those queries, ask whatever you like.Automatic Discovery: When your business integrates a new tool—like a CRM system or a marketing analytics platform—the MCP automatically broadcasts the new capabilities. AI agents immediately recognize these additions and autonomously start utilizing them. This means no downtime, no updates required for the agents, and no extra burden on your IT department.Flexible Integration: MCP uses a simple yet powerful format (JSON) already familiar to your technology teams. This ensures quick, straightforward, and low-cost integration.Future-Proofed Industry Standard: The recent announcement by OpenAI endorsing MCP as an industry standard highlights its significance. MCP, managed by Anthropic, ensures your investment today remains relevant tomorrow. It took less than 6 months from publication of the Protocol to be adopted by players such as Sharepoint, DropBox, and HubSpot. For example, imagine your business has recently integrated a new customer feedback system. Traditionally, making this feedback accessible to AI-driven analytics would involve extensive integration and manual configuration. With MCP, the moment this system becomes part of your infrastructure, AI agents automatically discover and start using the feedback data to enhance customer insights—immediately, seamlessly, and without requiring any additional configuration.<h3>What is the impact?</h3>For decision-makers, this means faster deployments, reduced costs, and greater agility in responding to market demands.In essence, MCP makes your business smarter: not by reinventing your tools, but by seamlessly enhancing how AI can utilize what you already have. AI agents won't replace your team but instead supercharge your people, empowering them with extraordinary capabilities to achieve more, faster by getting AI to work as their sidekick.Ready to see MCP in action and unlock your business’s full potential? <a data-mce-href="https://destrain.ai/contact" href="https://destrain.ai/contact">Contact us</a> today to start your journey.---The writer, Jonas Pomoell, is the founder and lead consultant at Des Train AI Oy, helping businesses harness the full potential of generative AI. Specializing in practical, transformative AI agent implementations, Jonas guides companies in streamlining processes, boosting productivity, and driving growth with tailored, advanced AI solutions.