snoller, Author at HybridAI

OpenClaw Alternatives for Enterprises (2026)

Let’s be honest: OpenClaw changed everything. When it first shipped, the idea of a single AI assistant that lives across all your messaging channels felt like science fiction. Today it’s table stakes. But as more teams move from “let’s try this” to “let’s deploy this for real,” the cracks in the original are showing — and a new generation of claw-like agents is stepping in to fill the gaps.

We’ve been building in this space for a while now, and we talk to enterprise teams every week who are evaluating their options. Here’s what the landscape actually looks like in 2026, and why we think it matters.

The Original: OpenClaw

You can’t have this conversation without starting here. OpenClaw is the most mature project in the space — 23+ channel adapters, a skills marketplace (ClawHub), voice wake mode, browser control, cron scheduling, Canvas with A2UI. It’s the kitchen sink, and for many teams, that’s exactly what they want.

But enterprise teams keep running into the same friction points. The ~500 MB memory footprint and 6-second startup feel heavy when you’re deploying hundreds of instances. The security model is application-level — permission checks in code, not actual OS isolation. And at ~400 source files with 53 configuration surfaces, onboarding new engineers takes longer than anyone admits.

If your team has the ops muscle and wants maximum channel coverage out of the box, OpenClaw is still a defensible choice. But if you’re evaluating with fresh eyes, keep reading.

NanoClaw: The Minimalist Thesis

NanoClaw took the opposite bet: what if the entire codebase was small enough that one engineer could understand it in an afternoon? It’s a single Node.js process, a handful of files, and true container isolation — not permission checks, but actual Docker or Apple Container boundaries per group.

The Agent Swarms feature is genuinely novel. NanoClaw was the first personal assistant framework to ship it, and it unlocks parallel task execution patterns that larger tools are still catching up on. The trade-off is ecosystem breadth — fewer channels, fewer integrations, and customization means changing code, not toggling config flags. For teams that want deep control and can live with five solid channels instead of twenty-three, NanoClaw punches well above its weight.

NullClaw: The Edge Play

This one is wild. NullClaw compiles to a 678 KB static Zig binary, runs in under 1 MB of RAM, and cold-starts in less than 2 milliseconds. On paper, those numbers shouldn’t be possible for something that supports 50+ LLM providers and 19 channels.

The architecture is vtable-driven — every component (providers, channels, tools, memory) is a swappable interface. You can compile exactly the feature set you need. For edge deployments, embedded devices, or that $5 ARM board sitting in a closet, nothing else comes close. The catch is Zig itself: smaller talent pool, steeper onboarding, and the project is still pre-1.0. Enterprise teams running fleets of lightweight agents on constrained hardware should absolutely evaluate this. Everyone else can admire it from a distance.

OpenFang: The Security-First Heavyweight

If NullClaw is the lightweight champion, OpenFang is the enterprise heavyweight. Written in Rust across 14 crates and 137K lines of code, it positions itself as an “Agent Operating System” — and the framing is earned. Sixteen distinct security layers including Merkle audit trails, taint tracking, Ed25519 signing, and SSRF protection. WASM-metered sandboxing for tool execution. Forty channel adapters.

The killer feature is Hands — pre-built autonomous capability packages for lead generation, OSINT collection, video processing, research, and social media management. These run 24/7 without user prompting, which is exactly what enterprise operations teams want. Cold start under 200 ms, 40 MB memory, 32 MB binary. The downside: it’s Rust, it’s complex, and it’s v0.3.30. But for organizations where audit trail and security posture are non-negotiable, OpenFang is the answer today.

CoPaw: The APAC Bridge

CoPaw comes from AgentScope and targets a gap that Western-built tools consistently miss: first-class support for DingTalk, Feishu, and QQ alongside the usual Discord/Slack/Telegram stack. Desktop installers for Windows and macOS, a web console for configuration, and Python-based skill authoring make it the most accessible option for non-developer users.

For enterprises with significant APAC operations — especially teams in China — CoPaw solves a localization problem that no other tool in this list even attempts seriously. The trade-off is the usual Python story: heavier runtime, less security hardening, more cloud dependency.

Hermes Agent: The Learning Loop

Nous Research’s Hermes Agent is the only project here that genuinely improves itself during use. It creates skills from experience, refines them over time, searches past conversations, and builds persistent user profiles. The closed learning loop — where the agent curates its own memory with periodic nudges — is architecturally distinct from everything else in this space.

For research teams and organizations betting on long-horizon agent deployments where accumulated knowledge is the moat, Hermes is uniquely compelling. It’s also the most research-oriented tool here, with built-in support for trajectory generation and RL environments. Less polished for day-one enterprise deployment, but the trajectory (pun intended) is clear.

HybridClaw: Where We Landed

We built HybridClaw because we kept seeing the same gap. Enterprise teams wanted OpenClaw’s feature depth but with actual security isolation, EU-stack compatibility, and GDPR-aligned data handling — without the ops burden of running Rust or Zig in production.

HybridClaw runs as a Node.js gateway with Docker-sandboxed tool execution. RAG-powered retrieval with document-grounded responses. Structured audit trails with hash-chain verification. Bundled office skills — PDF, XLSX, DOCX, PPTX — that handle the kind of document workflows enterprises actually need, not just chat. MCP integration for extensibility. Local model support via LM Studio, Ollama, or vLLM for air-gapped deployments. A built-in admin console with dashboard, session management, model configuration, and audit views.

What we think makes the difference: HybridClaw treats security and compliance as first-class architectural decisions, not afterthoughts bolted on top. Container isolation by default. Credentials separated from config. An onboarding flow that requires explicit trust model acceptance before anything runs. And all of it in TypeScript — which means your team can actually audit, extend, and maintain it without hiring Zig or Rust specialists.

Is it the smallest? No, that’s NullClaw. The most channels? No, OpenFang. The most autonomous? Hermes has that covered. But for European enterprises that need a production-ready agent with real security, document workflows, and a codebase their existing team can own — that’s the gap we built for.

The Takeaway

The “claw-like agent” space in 2026 is no longer a one-horse race. OpenClaw set the template, but the next generation is fragmenting along clear lines: minimalism (NanoClaw, NullClaw), security-first (OpenFang, HybridClaw), regional fit (CoPaw), and self-improvement (Hermes). The right choice depends on your constraints — not on who shipped first.

Pick the tool that matches your actual deployment reality, not the one with the longest feature list.

Beyond ChatGPT: Why HybridClaw Redefines the Rules for Enterprises

1. Introduction: The AI Productivity Paradox

In today’s business landscape, we observe a critical paradox: while individual employees achieve impressive efficiency gains using isolated tools like ChatGPT, the overall systemic performance of organizations remains stagnant. The result is an uncontrolled shadow IT environment that not only poses a significant risk to data sovereignty but also isolates valuable knowledge within private chat windows.

We are leaving the era of mere “AI experimentation” behind and entering the phase of industrial-grade AI. The decisive step moves away from pure personal productivity—as offered by OpenClaw (formerly MoltBot) in private use—toward a resilient enterprise infrastructure powered by HybridAI. Companies that still rely on simple chatbots are missing out on the potential of scalable operational excellence.

So why is a standard interface no longer sufficient for businesses? The answer lies in the transformation from isolated text generation to orchestrated, secure, and collective intelligence.

2. Takeaway 1: The Power of Orchestration – One Brain, Many Experts

A major obstacle to strategic scaling is dependence on a single provider. HybridAI solves this through multi-model orchestration. Instead of relying on a single model, the system leverages a portfolio of more than 10 leading LLMs—including GPT-5, Claude, Gemini 3, Mistral, DeepSeek, and specialized models like Nano-Banana for image generation.

At its core is Intelligent Task Routing: the system autonomously recognizes the nature of a task and delegates it to the most capable expert. While Claude designs complex coding structures, GPT-5 handles deep research, Gemini 3 excels at high-speed queries, and Nano-Banana visualizes concepts.

Strategically, this ensures full vendor independence: companies are no longer tied to the fate of a single provider but remain flexible and future-proof.

“Multi-model orchestration, shared RAG for departments, specialized tools, and maximum data security. Give your teams the most powerful AI assistance with full control.”

3. Takeaway 2: From Individuals to Collective Intelligence – Shared RAG and Team Memory

In traditional setups, AI acts as a “lone operator,” starting from zero with every interaction. HybridAI transforms this into a long-term digital memory for the entire organization. Knowledge transfer is revolutionized through two mechanisms:

Shared RAG (Retrieval-Augmented Generation): Departments or project teams work with shared knowledge bases. SOPs, specific guidelines, and technical documentation are centrally provided, allowing AI to respond based on verified company data.
Team Memory: The AI continuously learns from interactions across the entire collective. This shared intelligence ensures that valuable insights are not lost when a browser tab is closed but persist as a strategic asset for the entire department.

4. Takeaway 3: Sovereignty by Default – The EU Stack Guarantee

For compliance leaders, data security is the foundation of any AI strategy. HybridAI offers an architecture where sovereignty is not an option—it is the default.

The system is fully GDPR-compliant, entirely hosted in the EU, and already aligned with the upcoming AI Act.

For organizations with the highest security requirements, self-hosting via vLLM on their own infrastructure is also available. A key technical enabler is the multi-layer security concept: an integrated filter detects sensitive data (PII – Personally Identifiable Information) and automatically masks it before processing.

Combined with a complete audit trail that documents every action, the system meets even the most stringent regulatory requirements.

“Maximum AI power with maximum security”

5. Takeaway 4: Agents Instead of Chatbots – Autonomous Tool Usage

The paradigm shift of HybridAI lies in its ability to act. We are no longer talking about simple text generators, but about AI agents that can autonomously access browsers, APIs, databases, and ERP systems. These agents don’t just perform tasks—they complete them.

Specialized tools unlock their full potential, particularly in professional departments:

BI Service: Enables business intelligence via text-to-SQL. Employees can perform complex data queries in natural language without needing SQL knowledge.
Tax Classification: Automates highly specific processes such as assigning VAT codes, Incoterms 2020, and HS codes for global trade.
Coding Agent: Supports software teams with automated code reviews and testing directly within workflows—optionally secured with locally hosted models for maximum IP protection.

6. Takeaway 5: The Visual Identity of Hybrid AI

The HybridClaw logo is a visual metaphor for this new form of collaboration. The organic lines of the jellyfish represent fluid human intelligence and adaptability. These merge with mechanical claws and circuitry, symbolizing machine precision and connectivity.

A central element is the data cubes, manipulated by the mechanical claws. They represent the raw informational building blocks of an enterprise. The symbolism is clear: AI serves as a precise tool to structure unorganized data and transform it into valuable knowledge through the organization’s organic intelligence.

It is the perfect fusion of biological adaptability and technological power.

7. Conclusion: The Future of the Augmented Workforce

OpenClaw and the HybridAI infrastructure mark the end of isolated AI experimentation. This is not just another tool in the software stack—it is the operating system for the collective intelligence of modern enterprises.

In a world where information is the most valuable resource, the quality of orchestration determines market success.

Is your company still stuck in “chat mode,” or are you already leveraging the full potential of an orchestrated AI workforce for your strategic transformation?

Try it now: https://hybridclaw.io

How Can I Enhance My BI Data with AI?

Over the past few months, we’ve witnessed a real boom in AI applications – from ChatGPT to Copilot and specialized enterprise solutions. A question that keeps coming up: How can I intelligently connect my existing Business Intelligence data with AI?

The answer is simpler said than done. While AI excels at handling unstructured text, structured databases present a unique challenge.

The Problem: Structured Data Meets AI

Why RAG Isn’t the Solution

The classic RAG approach (Retrieval Augmented Generation) works brilliantly for documents, PDFs, or knowledge bases. Text is converted into vectors and searched semantically.

But: BI data in SQL databases is structured. It thrives on:

Precise aggregations (SUM, AVG, COUNT)
Complex JOINs across multiple tables
WHERE conditions with exact values
GROUP BY for groupings

A vector search over your revenue table will never match the precision of a SQL query. RAG is simply the wrong tool here.

Why Simple SQL Tool Calls Are Too Limited

The next thought: “Let’s just give the LLM an SQL tool!”

The problem with that:

Lack of context continuity: For every question, the model must re-understand the entire schema context
No iteration: Complex analyses require multiple cascading queries
Reasoning overhead: The conversational model must simultaneously write SQL AND provide clever answers
Prompt collision: SQL syntax and natural conversation compete for context space

A model that’s supposed to do both – SQL and conversation – won’t do either particularly well.

The Solution: A Two-Layer Approach

The elegant solution lies in specialization through layering. Instead of having one model do everything, we split the work between two specialized LLMs:

Layer 1: Text2SQL – The Data Translator

The first LLM has a single task: convert textual queries into precise SQL.

Benefits of this specialization:

Focuses solely on schema understanding and SQL syntax
Can be fed with extensive database context
No “distraction” from conversational requirements
Smaller, faster model possible (e.g., GPT-3.5, Claude Haiku)

Workflow:

User asks: “How much revenue did we make in Q4?”
Text2SQL LLM translates to: SELECT SUM(revenue) FROM sales WHERE quarter = 4
Query is executed, data comes back

Layer 2: Reasoning LLM – The Conversation Partner

The second, higher-level LLM is your intelligent analyst. It:

Conducts the conversation with the user
Decides what data is needed
Calls the Text2SQL LLM as a tool
Interprets the data and draws conclusions
Asks follow-up questions and conducts multi-turn analyses

Example Dialog:

User: “How is our revenue developing?”

Reasoning LLM thinks:
→ Need revenue data for recent quarters
→ Calls Text2SQL: “Revenue per quarter last 12 months”
→ Receives data: Q1: 1.2M, Q2: 1.5M, Q3: 1.8M, Q4: 2.1M

Reasoning LLM responds:
“Your revenue shows a clear upward trend with +75% growth over the last 4 quarters. Q4 was particularly strong with +16% vs Q3.”

User: “What’s driving that?”

Reasoning LLM thinks:
→ Need breakdown by product category Q4
→ Calls Text2SQL
→ Analyzes and responds: “The main drivers were…”

Why This Approach Is Superior

1. Separation of Concerns

Each LLM does what it does best:

Text2SQL: Precise SQL generation
Reasoning: Intelligent analysis and conversation

2. Better Performance

Smaller, faster models possible for Text2SQL
Less context-switching
Parallel optimization of both layers

3. Higher Quality

Text2SQL can be trained with detailed schema knowledge
Reasoning LLM focuses on insights, not syntax
Less “prompt pollution”

4. Easier Maintenance

Schema changes? Only adjust Text2SQL
Improve conversational style? Only adjust reasoning prompts
Clear responsibilities

5. Better Error Handling

SQL errors can be caught by the Text2SQL layer
Reasoning LLM can ask alternative questions
Graceful degradation possible

Implementation in Practice

At HybridAI, we implement exactly this approach for our clients:

Text2SQL Layer: A specialized model familiarized with your database schema
Reasoning Layer: Claude or GPT-4 for natural conversations about your data
Security: Row-level security and access control at DB level
Caching: Frequent queries are cached for faster responses

The result: Your employees can speak with your BI data in natural language – precisely, quickly, and intelligently.

Conclusion

Connecting AI with structured BI data is not a trivial task. Neither RAG nor simple SQL tools are sufficient.

The solution lies in intelligent division of labor: A specialized Text2SQL LLM translates queries into precise SQL, while a higher-level Reasoning LLM conducts the conversation and generates insights.

This two-layer approach combines the best of both worlds: The precision of structured queries with the flexibility of natural conversation.

Want to enhance your BI data with AI? At HybridAI, we support you in implementing intelligent data analysis solutions. Contact us for a non-binding conversation.

Evaluation-Set for every Customer

Today we launched a new feature in the Prompt-Tuning-Clinic – the “Evaluation Criteria” Section.

It’s one of the most annoying things in AI to hunt for the question whether a custom configured AI (ChatBot, Agent, Automation) is doing well or not. In most cases both suppliers and customers are treating it like this:

“Yesterday i did run this prompt against it, and it looked really well, good progress!” – or – “My boss asked it to do x and it gave a total wrong answer, we have to redo the whole thing!”

Its an inherent problem of AI to some extent, for one because of the universal capability of these systems and the fact that you can ask practically everything and will always get an answer. And – due to the non-deterministic architecture and functioning of these systems it is very hard to define what it is doing and what not.

We were a bit tired of this, and so we thought – why are we reading LLMarena (btw – we launched german LLM-Arena recently, try it here) and other rankings of new AI models every second day and dont apply similar mechanisms to our customer installations?

This is exactly what this new feature brings:

define a couple of test-prompts (you can upload some treatment material like your API-Documentation or an md-file of the Website and let the AI make proposals for test-prompts)
run these prompts against the current configuration of the bot
Evaluate them (can also be done with an LLM automatically)
Define correct answers for edge cases
Save those prompts that are important permanently
Give them thumbs up/down to create cases for Fine-Tuning and DSPy
Run them all to get a quality ranking

Once this is set up the game is changing drastically, because now we (both supplier and customer) do have a well defined test-set of intended behavior that can be run automatically.

This is not only good for initial setup of a system, but also for Improvements, Model-Updates, new Settings etc.

And: as we are also offering fine-tuning for our models and have integrated DSPy as automated Prompt-Tuning tool you can create training-data for these while creating your Evaluation-Set as well – just thumbs up/down on the answer creates an entry in the test-database for later.

Business Intelligence in the AI Era in 2026: Opportunities, Risks, and the Architecture Behind It

Let’s be honest: Does your company have all business-relevant information available at the push of a button? Or is it also stuck in various data silos, largely unconnected – the ERP here, the CRM there, plus Excel spreadsheets on personal drives and strategy documents somewhere in the cloud?

If you’re nodding right now, you’re in good company. I regularly speak with CEOs and finance leaders, and the picture is almost always the same: The data would be there. But bringing it together to answer a specific question takes days – if anyone can do it at all.

Why This Is Becoming a Problem Right Now

The days when companies could rely on stable markets and predictable developments are over. Inflation, geopolitical tensions, disrupted supply chains, a labor market in flux – all of this demands a new discipline: Decisions must not only be good, they must be good fast.

Traditional business intelligence has a proven answer to this: dashboards, KPIs, monthly reports. But let’s be honest – these tools hit their limits as soon as questions get more complex. What happens to our margin if we switch suppliers? How does a price increase affect different customer segments? What scenarios emerge if the euro keeps falling?

Questions like these need more than static charts. They need a real conversation with your own data.

The Temptation: An AI Sparring Partner for Your Decisions

This is exactly where generative AI gets really exciting. The idea is compelling: An intelligent assistant that knows your company’s numbers, understands connections, and lets you explore strategic options – anytime, without scheduling, without someone having to build an analysis first.

“How did our top 10 customers develop last quarter?” “What if we reduced the product portfolio by 20%?” “Compare our cost structure with last year and show me the biggest outliers.”

A dialogue like this would democratize business intelligence. Not just the controller with their Excel expertise would have access to insights – every decision-maker could query the data themselves. I still find this idea fascinating.

The Problem: When AI Hallucinates, It Gets Really Expensive

But – and this is a big but – here’s the crux. Large Language Models are impressive at generating plausible-sounding answers. They’re considerably less reliable at delivering factually correct answers. Especially when it comes to concrete numbers.

An AI that misremembers a date in a creative text? Annoying, but manageable. An AI that invents a revenue figure or miscalculates a margin during a business decision? That can really hurt. The danger multiplies because the answers are so damn convincing. We humans tend to trust a confidently delivered statement – even when it comes from a statistical language model.

I say this from experience: A naive integration of ChatGPT with company data is a risk, not progress. Anyone who sees it differently has either been lucky or hasn’t noticed yet.

The Technical Challenge: Connecting Three Worlds

The solution lies in a well-thought-out architecture that intelligently brings together three different data sources:

Structured data via SQL: The hard facts – revenues, costs, quantities, customer histories – typically reside in relational databases. Here, the AI must not guess but query precisely. The system must generate SQL queries, execute them, and correctly interpret the results. No room for creativity.

Unstructured data via RAG: Beyond the numbers, there’s context – strategy papers, market analyses, internal guidelines, meeting notes. These documents can be accessed through Retrieval Augmented Generation: The system searches for relevant text passages and provides them to the language model as context.

The model’s world knowledge: Finally, the LLM brings its own knowledge – about industries, economic relationships, best practices. This knowledge is valuable for interpretation, but dangerous when mixed with concrete company figures.

The art lies in cleanly separating these three sources and making transparent where each piece of information comes from.

The Solution: Everything into the Context Window

Modern LLMs offer context windows of 100,000 tokens and more. This opens up an elegant architectural approach: Instead of letting the model guess which data might be relevant, we proactively load all needed information into the context.

A well-designed system works in several steps: It analyzes the user’s question and identifies relevant data sources. Then it executes the necessary SQL queries. In parallel, it searches the document base via RAG. And finally, the LLM receives all this information served up together – with clear labeling of sources.

The language model thus becomes an interpreter and communicator, not a fact generator. It can explain numbers, reveal connections, ask follow-up questions, discuss options for action – but it doesn’t invent data, because the real data is already in the context.

Transparency as a Design Principle

Such a system must build transparency into its DNA. Every statement about concrete numbers should cite its source. The user must be able to trace: Does this come from the database? Was it quoted from a document? Or is it an assessment by the model?

This transparency isn’t just a technical feature – it’s the prerequisite for trust. Anyone basing business decisions on AI-supported analyses must know what they’re relying on.

The Path Forward

Business intelligence with AI is neither utopia nor hype – it’s an architecture challenge. The technology is mature, the models are powerful, the interfaces exist. What many companies lack is a thoughtful approach that leverages the strengths of LLMs without falling prey to their weaknesses.

The future belongs to systems that intelligently connect structured databases, document knowledge, and language models – while always making transparent what is fact and what is interpretation. Companies that find this balance gain more than just another analytics tool. They gain a real sparring partner for better decisions in difficult times.

And yes – that’s exactly what we’re working on.

🚀 HybridAI + N8N: Your AI Agent Just Got Seriously Agentic 🚀

Today marks a huge milestone for our HybridAI platform: we’ve fully integrated N8N – and it’s a game changer for anyone working with automation and intelligent agents.

What’s new?

🔗 Deep integration with N8N workflows
Every HybridAI user now gets free access to our dedicated N8N server. Even better: from inside any N8N workflow, you can now send a Function Call directly to your chatbot or agent – with a single click.

Example:
“Send a follow-up email to all leads from today.”
→ Your bot instantly triggers the corresponding N8N workflow.

Why does it matter?

Agentic AI means that your bot doesn’t just talk, it takes action. It can now handle complex workflows, launch services, update databases, and more – autonomously.

To achieve this, you need two things:

A smart control center → your HybridAI agent
A powerful action engine → N8N

Now you get both, perfectly connected.

What is N8N, anyway?

N8N is a no-code automation tool developed in Berlin. With it, you can:

Connect APIs and AI models
Read/write Google Docs
Send emails
Query or update databases
Build custom nodes for anything else

And now, your HybridAI chatbot can trigger it all seamlessly from any conversation.

How to get started?

If you have a HybridAI account, just go to your “AI Functions & Actions” section in the admin area and create a Function Call pointing to your N8N webhook. That’s it – your bot is ready to act.

🎯 Try it now and explore new levels of automation with HybridAI + N8N.

New IoT Integration: Real-World Data Meets Conversational Intelligence (2026 Update)

Update 2026: As of now, we also support MQTT sensor data. More importantly, we have connected our IoT sensor infrastructure to our BI solution. This means that data can now not only be read and reported, but also analyzed and evaluated in a multi-dimensional way.

We’re excited to introduce a powerful new feature on our platform: the ability to stream IoT sensor data directly into your chatbot’s context window. This isn’t about triggering an external API tool call—it’s about augmenting the bot’s real-time understanding of the world.

How it works

IoT sensors—whether connected via MQTT, HTTP, or other protocols—can now send live data to our system. These values are not fetched on-demand via function calls. Instead, they’re continuously injected into the active context window of your agent, making the data instantly available for reasoning and conversation.

Real-World Use Cases

🏃‍♂️ Fitness and Weight Loss

A health coach bot can respond based on your real-time activity:

“You’ve already reached 82% of your 10,000 step goal—great job! Want to plan a short walk tonight?”

Or reflect weight trends from smart scales:

“Your weight dropped by 0.8 kg since last week—awesome progress! Should we review your meals today?”

⚡️ E-Mobility and Charging

A mobility assistant knows your car’s charging state:

“Your battery is at 23%. The nearest fast charger is 2.4 km away—shall I guide you there?”

Bots can also keep track of live station availability and recommend based on up-to-date infrastructure status.

🏗 Accessibility and Public Infrastructure

A public-facing city bot could say:

“The elevator at platform 5 is currently out of service. I recommend using platform 6 and taking the overpass. Need directions?”

Perfect for people in wheelchairs or with limited mobility.

🏭 Smart Manufacturing and Industry

A factory assistant can act on process data:

“Flow rate on line 2 is below target. Should I trigger the maintenance routine for the filter system?”

This allows for natural language monitoring, error detection, and escalation—all in real time.

What Makes This Different?

🔍 Contextual Awareness, Not Tool-Calling
Sensor data is part of the active reasoning window—not fetched via a slow external call, but immediately available to the model during inference.

🤖 True Multimodal Awareness
Bots now reason not just over language but also over live numerical signals—physical reality meets LLM intelligence.

🚀 Plug & Play Integration
Bring your own sensors: from wearables to factory machines to public infrastructure. We help you connect them.

In Summary

This new feature unlocks unprecedented potential for intelligent agents—combining the power of conversational AI with a live, evolving understanding of the physical world. Whether you’re building a wellness coach, a mobility assistant, or an industrial controller, your agent can now think with real-world data in real time.

Reach out if you’d like to get started!

A practical view on agentic AI and why we think MCP is not solving a relevant problem.

Yes, in the current AI hype discourse this statement almost feels like suicide, but I want to briefly explain why we at HybridAI came to the conclusion not to set up or use an MCP server for now.

MCP servers are a (currently still “desired”) standard developed and promoted by Anthropic, which is currently gaining a lot of traction in the AI community.

An MCP server is about standardizing the tool calls (or “function calls”) that are so important for today’s “agentic” AI applications – specifically, the interface from the LLM (tool call) to the external service or tool interface, usually some REST API.

With the current ChatGPT image engine generated – I love these trashy AI images a little and will miss them…

At HybridAI, we have long relied on a strong implementation of function calls. We can look back on a few dozen implemented and production-deployed function calls, used by over 450 AI agents. So, we have some experience in this field. We also use N8N for certain cases, which adds another relevant layer in practice. Our agents also expose APIs to the outside world, so we know the problem in both directions (i.e., we could both set up an MCP server for our agents and query other MCPs in our function calls).

So why don’t I think MCP servers are super cool?

Simple: they solve a problem that, in my opinion, barely exists and leave the two much more important problems of function calls and agentic setups unsolved.

First: Why does the problem of needing to standardize foreign tool APIs hardly exist? Two reasons. (1) Existing APIs and tools usually have REST APIs or similar, meaning they already use a standardized interface. These are quite stable, which you can tell from API URLs still using “/v1/…” or “/v2/…”. They remain stable and accessible for a long time. Older APIs are often still relevant – like those of the ISS, the European Patent Office, or some city’s Open Data API. These services won’t offer MCP interfaces anytime soon – so you’ll have to deal with those old APIs for a long time. (2) And this surprises me a bit given the MCP hype: LLMs are actually pretty good at querying old APIs – better than other systems I’ve seen. You just throw the API output into the LLM and let it respond. No parsing, no error handling, no deciphering XML syntax. The LLM handles it reliably and fault-tolerantly. So why abstract that with MCP?

In reality, MCP adds another tech layer to solve a problem that isn’t that big in daily tool-calling.

The bigger issues are:

–> Tool selection

–> Tool execution and code security

Tool selection: Agentic solutions work by allowing multiple tools, sometimes chained sequentially, with the LLM deciding which to use and how to combine them. This process can be influenced with tool descriptions – small mini-prompts describing functions and arguments. But this can get messy fast. For example, we have a tool call for Perplexity when current events are involved (“what’s the weather today…”), but the LLM calls it even when the topic is just a bit complex. Or it triggers the WordPress Search API, though we wanted GPT-4.1 web search. It’s messy and will get more complex with increased autonomy.

Tool execution: A huge issue for scaling and security is the actual execution of tool code. This happens locally on your system. Ideally, at HybridAI, we’d offer customers the ability to submit their own code, which would be executed as tool calls when the LLM triggers them. But in terms of code integrity, platform stability, and security, that’s a nightmare (anyone who submitted a WordPress plugin knows what I mean). This issue will grow with more use of “operator” or “computer use” tools – as those also run locally, not at OpenAI.

For these two issues, I’d like ideas – maybe a TOP (Tool Orchestration Protocol) or a TEE (Tool Execution Environment). But hey.

Agentic Chatbots in SaaS – How HybridAI Makes Your App Smarter

SaaS platforms have long included help widgets, onboarding tours, and support ticket systems. But what if your app had a conversational layer that not only explained features – but also triggered them?

With HybridAI, this is now possible. Our system enables you to create agentic chatbots that speak your domain language, understand user intent, and call backend functions directly via Function Calls and Website Actions.

From Support Widget to Smart Assistant

Traditional support widgets are passive: they answer FAQs or forward tickets. A HybridAI bot, however, can do things like:

Trigger onboarding steps (“Show me how to create a new project”)
Fetch user data (“What was my latest invoice?”)
Execute actions (“Cancel my subscription”)

All of this is powered by safe, declarative function calls that you define – so you stay in control.

How It Works

Define Actions: You provide a list of available operations (e.g. getUser, updateRecord, createInvoice) and their input parameters.
Connect via API or Function-Call Interface: HybridAI receives these as tools it can call from natural language.
Bot Instructs + Responds: The chatbot interprets the user prompt, selects a matching function, fills in parameters, and calls it.
Real-Time Feedback: The user receives immediate confirmation or result, without ever leaving the chat.

Integration Benefits

No coding required to get started – Just define what your functions do.
Frontend or backend integration via JS events or APIs
Custom styling + voice – the bot looks like part of your product
Multi-language and context-aware – excellent for international SaaS

Use Cases

CRM assistants that update leads or pull sales data
Analytics bots that explain dashboards or alerts
HR bots that automate time-off requests
Support bots that resolve issues without agents

Ready to Try?

You can test HybridAI’s function-calling capability today with our Quickstart Bot – no sign-up required.

And if you’re ready to bring this into production, reach out to us – we’ll help you integrate HybridAI into your stack in days, not months.

Real-life use at school

This week we tested HybridAI for the first time in a real school environment. The students of Stadt-Gymnasium Köln-Porz had the opportunity to spend a German lesson with us under the guidance of Sven Welbers – on the wonderful topic: Grammar!

What could possibly be better!

It was genuinely exciting, as we configured HybridAI according to the teacher’s specifications to present a detective story that could only be solved step by step by completing grammar exercises. Since the stories were generated by the AI, each student had a unique version, with delightful variations even when new stories were generated.

Throughout the lesson, the bot provided feedback on progress and occasionally injected humorous messages.

Conclusion: The students certainly had a lot of fun! Not always guaranteed with such topics. The teacher was impressed by the educational quality of this lesson. Despite the dry material, the students appeared engaged and focused.

In the near future, we will develop further examples for the educational sector. The next session with a bot on the topic “Konjunktiv I and II” is already being prepared!

You can see the grammar bot in action here: