Video Transcript
[0:00] je Strategische SNC naar een paar jaar al op de gider. Maar tot vandaag weer bij wens Peter 26 en devolutie Cash At Number is gewoon skyrocketing. sye ik zei trainers instantier entج vers которых in de tinggelde
[0:41] L pay wholeing in de UK net ochtend als kissời en op de schijneres dieressed die je hier maar wat uitgegeten worden is punya nairelea, niet waarom hij dit vijf dating horters zullen trekken. Daarom is het z het is een resschu Porkchottoin, voor de magnomanse aan en mediten Hooding de
[1:36] 22 persoonchlag door presenting. Chairman, mijn scherm overge cenningd, posted relationships met de importance 서ホ Lijker zeg maar heel fier. Ja, maar veroogde het client' Levi dat mandelijk en dan manuelie, koop je de output in de geemoel.
[2:10] De manuel was de one doing all the logistische werk. De leidde de volgende stepp op de volgende, verantwoordelijk in de vliegde de SINGLE Agent systeem. Dit is toen het ingeneerde onderzoek het concept called function calling. Right, een grote geel. Het is een geelde geelde, het was een geelde, het een AI om een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geelde, een geeling zelf maar het was nog een ultra linear proces. Hele drills die van de imponer,
[2:41] a�� is de prevail aan het logische einde te en als het een geelde vertheymering is dus of defineusgegeluilig aan het stylding. En dat brengt naar het specsuzepuntende journal en net de eedeldstippen nog tegen de hele Phewwerken van het geelde Gleichensch� en het is een heldly-advanced calculator. Oké, ik heb dat een elejag. Ja, het kan wel complex fysiekse equations in seconden, maar het werkt only om om een human fysiek te zitten en de punten in de nummer te worden. Maar een multi-agent eco-system.
[3:11] Dat is een digitale symplelein. Oh, perfect. Je hebt een manierhandig om specific te hebben, specialiswerkers, dezelfde laborer, en dan kwaliteit inspecteren, checking de output op het eend. En ze zijn er automatisch communicatie met each other. En de economische realiteit van dat symplelein wat het opgevallen is, de massive adoptie en projectie we zien. PWC's AI Predictions voor 2026 show dit specific market segment groeien bij een 46% compound annual growth rate. Wow, 46%.
[3:41] Ja, want als je een specialisagent collabing en een evaluatie intermedeant resulten en verwerking met each other, die niet alleen de vragen ergens vragen. Er zijn er ergens complex, multi-step werkvloos en entirely op hun eigen. Dus hoe gaan we eigenlijk deze digitale symplelein building building in? Als ik een CTO en ik deze machines aan het verwerking van mijn businessproces wat is onder de hoed van een productie-grade system? Let's start with a reasoning engine, de brain. Nou, in een multi-agent setup, je niet alleen gebruikt een massive model
[4:11] voor alles. Je mag de model op de specifice rol op de symplelein. Dus Claude van Anthropic heeft deze massive favorit voor een hele structuurrezening en het is om het complex logisch te doen. Maar op de andere hand, je kunt gebruiken openAI's GPT-40 en O3 models voor broad multimodel tasks zoals procesing images en unstructuurde text. En voor een hevige data. Dat is waar Google's Gemini 2.5 komt in. Het is met een massive contextwondo
[4:41] om een hevige lifter voor een technisch datacet. Dat is een company's entire financiele historie. Het zie je rapid van het eind explained. Maar een muziekle adornag eind deze actie is geen du way ze actie eind blijpen deze interactie van die bekingspre tussen de Schüler van de wereld. Parf 쓰는 van de hardware. Je moet een availke actie
[5:12] waarin het 컨 spelling auto기가anstaging l summer coding. De eventueel código speelt mij inالder losese cornerAedi ek van centro switch van dit punt voor dit planimer. If an agent tries to pull a report and the server times out, a linear change just breaks. Langgraph allows the agent to loop back.
[5:44] It reads the error log, realizes the server is down, rewrites its own plan, and maybe tries a backup database instead. So it provides the logical pathways for trial, error and correction? Precisely. Now there are others out there too, depending on the team structure you need. The source mentions crew AI, which is structured much more like a traditional corporate org chart. Strictly define roles in hierarchies. Microsoft's auto-gen is in there too, right? Yeah, auto-gen is very popular for complex multi-agent conversations and research debates.
[6:14] The framework you choose depends entirely on how you need your digital workers to interact. But regardless of the skeleton, they still need to touch the real world. I mean, they need to update the CRM, pull files, send messages. How are they doing that without engineers having to build custom integrations for every single app in the company? Ah, this is where the landscape completely changed recently. The standard in 2026 is the model context protocol or MCP. Okay, break that down for us. Well, to understand why MCP is revolutionary, look at how we used to build software.
[6:45] If you wanted a program to talk to a Salesforce, JIRA and Slack, you had to write custom API connectors for all three. And if Slack updated their API, your code broke. A massive maintenance nightmare. But MCP acts as a universal adapter. It's an open standard. Think of it like a USB-C cable for AI. Oh, I like that. Yeah, instead of building custom plugs for every device, you just use MCP. And suddenly your agent can interact with almost any external service securely and consistently. Your digital hands can pick up any tool anywhere.
[7:18] And that completely eliminates vendor lock-in. Like if I want to swap out Salesforce for HubSpot next year, I don't have to rewrite my entire AI agent from scratch. Yeah. I just plug the MCP into the new tool. Exactly. Okay, so we have brains, a skeleton, and hands. What about memory? If an agent is running a complex week-long research task, how does it not just forget what it did on Monday? Because language models essentially have the memory of a goldfish once their context window fills up. Right.
[7:48] Which is why a production system requires a multi-layered memory hierarchy. Working memory handles the immediate context of the current task. But the real magic happens in episodic and semantic memory. Let's talk about episodic first. Episodic memory is the ability to remember past interactions and outcomes. So if the agent ran a specific database query yesterday that returns zero results, episodic memory ensures it remembers that failure. It won't waste time and compute power trying the exact same query today. That makes total sense for learning from mistakes.
[8:19] But what about semantic memory? The source mentions something called rag, or retrieval augmented generation, using vector databases. I'm not sure if I'm going to be able to get a lot of jargon for someone who might just be stepping into this space. Let's demystify that a bit. Sure. Think of R like giving the AI an open book test. A language model is trained on public data up to a certain date, but it doesn't know your company's proprietary employee handbook. Are the new pricing sheet you've literally just published this morning? Exactly. So a vector database takes all your company's internal documents,
[8:50] chops them up, and turns them into mathematical coordinates. And the AI needs an answer, or I allows it to instantly flip to the exact page of your internal documents, retrieve the current information, and use that to generate its response. Instead of relying on its outdated training data, I love the open book test analogy. So, okay, we have brains, a skeleton, hands with universal USB-C adapters, and an open book memory. Now, I have to jump in with the reality check here. Go for it. We're describing a system where autonomous agents can access live databases,
[9:22] rewrite their own plans, and take actions in the real world. As a CTO, that is my absolute nightmare scenario. It is definitely intimidating. Yeah. We've all seen AI confidently hallucinate math, or make up legal precedence. Why should I trust a multi-agent system not to take down my entire production server, or get stuck in a loop and bankrupt my monthly API budget in a weekend? That is the most critical hurdle for enterprise adoption. You cannot just deploy these systems and hope for the best.
[9:53] The solution to that fear is what the industry calls governance as code. Meaning it's not just a written policy in an employee handbook that the AI might ignore. No, it is hard-coded into the architecture itself. Think of it like a physical governor on a sports car's engine. You physically cannot push the car past a certain speed. Okay, give me an example. Budget limits are a great one. You hard-coded strict maximum compute allowance per agent per task. If the agent gets confused and starts looping, it hits that micro-budget limit and the system immediately cuts its power. Okay, that solves the week in bankruptcy issue.
[10:25] What about the deploying broken code to the live server issue? Mandatory approval flows. The agents are designed to do 99% of the heavy lifting. But for any high-impact action deploying code, sending a mass email to a thousand clients, transferring funds the system automatically pauses. And waits for a human. Exactly. It packages up the work, provides an audit trail of exactly how it reached its conclusion, and waits for a human to click a proof. Furthermore, in Europe, the EUAI Act requires rigorous transparency and risk assessment.
[11:00] Governance as code natively logs every decision pathway, ensuring you are legally compliant and completely secure. The human and loop safety net makes a lot of sense. So we built the machine safely. Now I want to see it actually work. How does this differ from traditional automation? A lot of people hear automated workflows in a media think of tools like Zapier. You know, if an email comes in with an attachment, save it to Dropbox. How is an agentic system different from that? Traditional tools like Zapier are fantastic, but they are brittle. They follow rigid, predefined paths.
[11:30] If you set a rule to save an email attachment, but the sender forgets the attachment, and instead includes a Google Drive link in the body of the email, the traditional workflow breaks. Right, because it doesn't know what to do with the link. Exactly. A multi-agent system, however, is adaptive. If it encounters a link instead of a file, it reasons. I need a file, but I have a link. I will use my web browsing tool, navigate to the link, download the file, and then save it to the drive. It adjusts to unexpected roadblocks autonomously.
[12:00] That adaptability is a massive distinction. Let's look at how that plays out in the real world. The source highlights code deployment as a major use case. For the engineering team's listening, walk us through how a multi-agent system handles a pull request. Traditionally, a developer finishes a feature and submits a pull request. A senior engineer then has to manually review the code line by line, looking for bugs, security flaws, or style guide violations. It takes forever. It really does. But in multi-agent setup, a review agent instantly intercepts that pull request.
[12:32] It analyzes the code against your company's specific guidelines. If it spots an issue, it doesn't just flag it. It can actually suggest the exact code fix. Then, a test agent generates edge-case tests on the fly and runs them. And once it passes all those autonomous checks, it just waits for the human to hit approve for the actual deployment. Yeah. Engineering teams utilizing this are seeing a 40% reduction in release cycle times, because they are eliminating the bottleneck of manual peer review. Another great example from the source is content production.
[13:03] Aetherlink actually runs their own marketing this way. Their AI insights blog operates on a digital assembly line. Right, they use their own tech. Yeah, they have a research agent that scans the web for trending topics in the AI space. It hands that data to a writer agent that drafts the post. Then, an SEO agent comes in and optimizes the headers and keywords. Finally, an editor agent reviews it for factual accuracy and tone. They are publishing three high-quality articles a day using this system. And think about financial reporting, too.
[13:33] Gathering data from siloed departmental databases, analyzing it for anomalies, checking it against current compliance regulations, and drafting the monthly summary. It usually takes a finance team several grueling days at the end of every month. But an agentic system compresses that exact workflow into hours. A data agent pulls the numbers and analysis agent spots the trends. A report agent drafts the text, and a compliance agent double checks the math. Seeing those use cases, I mean, the time-saved is incredible.
[14:03] But implementing this sounds really daunting. How does the company actually integrate this without getting trapped by a single vendor or, you know, spending millions? Let's look at how Etherlink is structuring this for their European clients. Their whole philosophy is built on a very specific rule. Right. Be protocol first, not framework first. Break that down. Why does that distinction actually matter? Because frameworks evolve so quickly. What is industry standard today? It might be completely obsolete in two years. If you hard-code your entire business logic into one specific framework, you're trapped.
[14:35] Ah, I see. But by building on open protocols like MCP for tools, and open agent-to-agent communication protocols, you future-proof the system. You can swap out the underlying brain or framework later without rebuilding your business processes from scratch. That makes total sense. And Etherlink breaks their offerings into three main lines, right? Etherbot for the actual AI agents. Ethermind for the high-level strategy in consulting. And EtherDeV, which is their internal development platform. Right. And the EtherDeV platform is really the key to their speed.
[15:06] Because they have pre-built agent templates and MCP integrations ready to go. They are delivering these complex multi-agent ecosystems in weeks, rather than the months usually takes to build from scratch. They're also launching something called a Gora. And for any European CTE-listening, this is huge. It's essentially an app store for digital workers. A merge of place where you can discover and deploy specialized AI agents. Yes. And the critical feature is that it is fully EU sovereign. It's fully GDPR compliant and runs entirely on European infrastructure.
[15:38] Which is such a big deal. Data sovereignty is a massive hurdle in Europe. You cannot have proprietary financial data or customer records being processed on unknown servers in another jurisdiction. Agora solves that compliance headache natively. Let's talk economics. What does it actually cost to hire this digital workforce? The Aitorlink source provides some surprisingly transparent pricing. They do. For a single, highly specialized custom agent, the investment is roughly 5 to 10,000 euros. For a worker that never sleeps, never takes a vacation, and scales infinitely.
[16:11] I mean, that's a rounding error for enterprise budgets. Exactly. And if you want to deploy a full multi-agent ecosystem, a team of 5 to 10 collaborating agents, complete with the memory architecture, the monitoring, and the governance as code safety nets we discussed, you're looking at 25 to 75,000 euros. Aitorlink operates at a transparent 225 euro hourly rate to build it out. When you compare a 75,000 euro one-time build to the recurring annual salaries of a 10 person department doing manual data entry or basic research,
[16:43] the ROI is impossible to ignore. It really is. So, as we pull all these threads together, let's distill it down. What is the fundamental takeaway for the leaders listening today? Simply put, Agentec AI is not some futuristic science fiction concept. It is the technological reality of 2026. We are moving from single reactive models to autonomous multi-agent architectures right now. The train has left the station. Exactly. Organizations that invest in and deploy these digital assembly lines today are building an operational lead that will be mathematically impossible for their competitors to catch up to tomorrow.
[17:21] It is an exponential advantage. And for me, my biggest takeaway is how this fundamentally changes the human experience of work. The goal here isn't to replace the knowledge worker, it's to elevate them. We are all going through a massive paradigm shift. You are becoming an agent supervisor. Yes. You are typing every email or running tedious Excel analyses manually. Your daily job is now defining strategic goals, monitoring progress and steering a team of tireless digital specialists. But you know that shift to supervision raises a fascinating and somewhat daunting secondary challenge.
[17:52] Well, if companies can suddenly compress days of grueling financial analysis or junior level coding into mere hours using an agentec system, what happens to the entry level roles where humans traditionally learned how to do those jobs in the first place? If the AI is doing all the junior work today, how do we train the senior managers and strategic supervisors of tomorrow? Wow. That is a massive question to chew on. From the calculator to the digital assembly line, the tools have evolved, but we are still the ones responsible for running the factory. We just have to figure out how to train the next generation of factory managers.
[18:24] Thank you for joining us on this deep dive. More AV insights at etherlink.ai.