Scaling is often described as a destination.

In practice, it’s a condition you operate under long before anyone labels it.

At Incerro, we’ve spent years working on systems that are meant to last - across evolving requirements, changing teams and business contexts that don’t wait for clean redesigns. These aren’t short-lived experiments. They’re systems that are expected to endure.

What 2025 did wasn’t introduce new problems.

It amplified the ones that always matter when software is allowed to live long enough to matter.

Scale Rarely Breaks Systems. Change Does.

Very few of the systems we touched in 2025 struggled because of load.

What surfaced instead was resistance:

changes that felt heavier than they should,

features that took longer not because they were complex,

but because the system pushed back.

The pattern was consistent across domains:

as software lives longer, the cost of change becomes the real bottleneck.

This isn’t a failure of engineering. It’s the natural outcome of systems accumulating assumptions over time. Scale exposes those assumptions - not through traffic spikes, but through evolution.

The systems that held up best weren’t the ones optimized for peak scenarios.

They were the ones designed to absorb change without forcing rewrites.

Architecture Didn’t Fail - It Drifted

In 2025, architectural problems rarely announced themselves loudly.

There were no dramatic collapses. Instead, there was erosion:

boundaries that slowly lost alignment,

decisions that made sense once but quietly outlived their context,

areas of the system engineers hesitated to touch.

By the time friction became visible, the architecture had already drifted.

This pushed us to stop asking whether a design was good and start asking whether it was still true. Architecture stopped being something you “get right” and became something you continuously validate against how the system is actually used.

Where This Became Personal

We at Incerro, felt this most clearly while working on the architecture for Conformiq’s new SaaS platform.

The mandate wasn’t to build something impressive. It was almost the opposite.

The goal was to design a system that would:

• remain understandable years from now
• support product evolution without structural churn
• quietly accommodate future AI-driven capabilities without being tightly coupled to any one approach

The result is, by design, not exciting to look at.

It’s intentionally boring.

Clear boundaries.

Predictable flows.

Explicit tradeoffs.

That boredom is the point. It’s what allows the system to age well - and what makes future capabilities possible without forcing architectural reinvention. 2025 reinforced that the most scalable decisions are often the least flashy ones.

Developer Experience Set the Upper Bound on Velocity

Across teams, the fastest progress didn’t come from writing code faster.

It came from reducing cognitive load.

The systems that moved well had familiar traits:

state lived in obvious places,

data flows were predictable,

failures were explainable without archaeology.

Where developer experience degraded, velocity followed - regardless of team size or talent. By 2025, it was hard to ignore that developer experience isn’t a productivity concern; it’s a scaling constraint.

Software scales only as far as the people working on it can reason about it.

Optimization Without Flexibility Aged Poorly

Performance still matters. But 2025 made one thing clear: optimizing the wrong abstraction narrows the future.

We saw systems that were highly tuned but brittle, where every optimization locked in assumptions that no longer held. Meanwhile, systems that favored flexibility - even at a small performance cost - continued to adapt.

The systems that endured weren’t the fastest.

They were the ones that left themselves room to change their mind.

Ownership Was the Only Boundary That Never Drifted

As systems crossed team boundaries, technical structure alone stopped being enough.

The most resilient systems had something else in common: clear ownership.

When domains changed, responsibility was unambiguous.

When behavior was unclear, there was accountability for clarifying it.

Where ownership blurred, systems degraded faster - not from neglect, but from diffusion of responsibility. By 2025, the pattern was unmistakable: architectural boundaries without ownership don’t hold.

Observability Became How We Trusted Systems

As systems grew, intuition stopped scaling.

Observability didn’t just help with debugging; it changed how decisions were made. Architecture that couldn’t be observed was harder to defend. Systems that surfaced their behavior stayed aligned longer.

You can’t scale what you can’t see - but more importantly, you can’t trust it.

The Systems That Lasted Felt Uneventful

The most unexpected pattern of 2025 was this:

The systems that held up best weren’t clever.

They weren’t trendy.

They didn’t try to impress.

They were predictable.

Explicit.

Calm under change.

That uneventfulness wasn’t accidental. It came from restraint, revisiting assumptions and designing with future engineers in mind.

Boring systems age well.

Where This Leaves Us

Scaling in 2025 wasn’t about size.

It was about endurance.

Endurance against change, team turnover and evolving business realities.

At Incerro, these lessons didn’t arrive as sudden realizations. They emerged repeatedly, across systems, until the patterns were impossible to ignore.

The real measure of scale isn’t how much a system can handle today.

It’s how long it can keep adapting tomorrow - without asking for a rewrite.

AI and ML systems don’t really exist as single models anymore. In practice, they turn into collections of moving parts-training jobs running quietly in the background, inference services handling real users, data pipelines shifting information around, vector databases storing context and agentic workflows trying to keep everything coordinated. All of this runs at the same time and rarely in neat or predictable ways.

Once these systems are exposed to real usage, the problems start to look different. Model architecture matters less than expected. Instead, teams deal with traffic spiking without warning, GPUs already under pressure, or small issues that slowly affect other services.

This is usually the point at which Kubernetes becomes genuinely useful. It adds structure where things would otherwise get messy, keeps environments consistent, and removes a lot of infrastructure friction so teams can focus on how their systems actually behave under real conditions.
At Incerro, Kubernetes is foundational. It sits at the core of complex AI platforms, helping to keep things steady as workloads move fast and don’t behave the way you expect them to.

Scaling AI Services Based on Demand

AI workloads don’t behave like traditional applications. Training workloads can hold on to GPUs for long stretches of time, while inference services need to respond immediately when traffic spikes. Treating both the same usually leads to inefficiencies - or cloud costs that only become visible much later.
Kubernetes helps by allowing different workloads to behave differently, instead of forcing everything into the same scaling pattern:

• Inference services can scale up quickly when traffic increases
• Training jobs continue running in the background without interruption
• GPU resources are scheduled more deliberately instead of sitting idle

This approach keeps performance predictable without pushing teams into constant over-provisioning.

Agentic Workflows and MCP in Practice

Many modern AI systems are now agentic by design. Multiple agents collaborate to plan steps, call tools, and share context. As more agents are introduced, coordination naturally becomes harder to manage.
Kubernetes helps by giving each agent a clear service boundary. MCP (Model Context Protocol) fits into this setup by providing a consistent way for agents to access shared context and tools, while Kubernetes quietly handles service discovery and networking behind the scenes.
At Incerro, this makes experimentation safer. Teams can add, remove, or adjust agents without worrying that a single change will destabilize systems already running in production.

Managing the AI Lifecycle on Kubernetes

Kubernetes isn’t just about deployment. It supports the entire AI lifecycle—from training and experimentation to rollout and ongoing updates. Tools like Kubeflow and MLflow integrate naturally into this ecosystem without locking teams into rigid platforms.
When rollout strategies are combined with proper observability, teams start to notice clear improvements:

• New versions ship with minimal disruption
• Performance and resource usage become easier to track
• Failures stay contained instead of cascading

That level of reliability matters more as AI systems become user-facing and expectations continue to rise.

Why Kubernetes Makes Sense for AI Teams

Kubernetes doesn’t try to understand models, prompts, or algorithms. It focuses on infrastructure, scaling, and reliability—things most AI teams don’t want to rebuild from scratch.
Whether it’s simple inference APIs, MCP-driven tools, or complex agentic workflows, Kubernetes provides a consistent operational foundation. That balance of flexibility and control is why it keeps showing up in large-scale AI systems.
At Incerro, this shows up in small but important ways: less time spent dealing with infrastructure issues and more time improving systems that users actually depend on.

Where This All Leads

Kubernetes has become the orchestration layer many modern AI systems rely on. It enables demand-driven scaling, agentic workflows, and integrations with tools like Kubeflow and MCP—without adding unnecessary complexity.
By keeping infrastructure concerns out of the way, Kubernetes frees teams to focus on what really matters: turning AI ideas into stable, production-ready systems.

AI-powered experiences - from chatbots to virtual assistants - have become increasingly sophisticated. However, they remain isolated from live enterprise data, meaning they often can’t access the most current information in databases, documents, or business applications. In practice, every new data source or tool (CRM, ERP, file storage, etc.) has required its own custom connector. This creates a tangled “M×N” problem: connecting M AI clients to N data systems results in M×N integrations. The result is brittle, one-off solutions that don’t scale. To break out of these silos, AI experiences need a standardized bridge to back-end systems. The Model Context Protocol (MCP) provides that bridge, offering a unified way for AI agents to discover and securely interact with real business systems.

The Data Challenge

Modern AI models (LLMs) are powerful reasoners, but they only know what’s in their training data or what’s manually provided at runtime. In an enterprise setting, much of the critical context lives in proprietary systems (customer databases, supply-chain apps, internal wikis, etc.). Today, giving an AI assistant access to those systems means writing custom “glue code” for each one. This leads to three key issues:

• Information: Valuable company data is locked behind separate APIs and legacy interfaces. No single AI model can natively see across them.
• Integration complexity: Developers must build and maintain custom connectors for every AI/data pairing, which is time-consuming and error-prone.
• Scalability limits: As the number of AI tools and data sources grows, the integrations multiply. Without a standard, you get an unmanageable M×N matrix of connections.

In short, enterprises end up with many capable AI tools that simply cannot tap into real-time business context. This severely limits their usefulness. For example, a helpdesk AI might generate answers based on general knowledge but cannot fetch the latest customer order status from a CRM without a bespoke integration.

Introducing the Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard designed to solve this integration problem. Think of MCP as a “universal adapter” or standard interface that lets AI systems plug into external data and services. Developed by Anthropic and now open-source, MCP defines how an AI agent can discover and use tools, data sources, and prompts in a consistent way.

Concretely, MCP works with a client-server architecture:

• MCP Clients: These are AI applications or agent frameworks (for example, a chatbot, IDE assistant, or automation platform) that include an MCP client component. The client drives the AI model and initiates connections.
• MCP Servers: These sit between the AI and the real systems. Each server wraps a particular data source or service (like a database, API, or document repository) and publishes its capabilities over MCP. These capabilities include tools (functions the model can call), resources (data to include in context), and prompts (predefined query templates).

When an MCP-enabled AI starts, it queries connected servers to discover available tools and data. The server responds with structured metadata: descriptions of each tool/function, required parameters, and permission rules. The AI agent can then “call” these tools with JSON-formatted arguments. The server executes the requested action (for example, running a database query or retrieving a document) and returns the result in a machine-readable format.

This dynamic, discovery-driven model is fundamentally different from calling fixed REST APIs. Instead of hard-coding endpoints and payloads, the AI can explore what services exist and invoke them on-the-fly. In effect, MCP turns an AI from a closed system into an agentic workflow engine: it can reason about what tools to use and chain multiple steps across different back-end systems. As Stibo Systems explains, MCP is “the bridge between reasoning and real-world action” that lets AI agents interact with enterprise data securely and at scale.

How MCP works: Discovery and Calling

Under MCP, every connection begins with self-describing tools. When a server starts, it “announces” each available function: what it does, what parameters it needs, and what kind of response it returns. For example, a Slack server might register a postMessage(channel, text) tool, or a database server might register queryDatabase(queryString). The AI client asks the server, “What can you do?” and receives a catalog of these tools and data resources.

The AI model (or agent) can then pick which tools to use. It reads the descriptions to decide which function applies, fills in the required parameters, and invokes the tool via the protocol. Because all communication is in a standard format (typically JSON-RPC), the model doesn’t have to deal with different APIs or data formats for each service. The server handles authentication, execution, and returns the result back to the model.

This discover-then-invoke loop can repeat many times, enabling complex multi-step workflows. For instance, an AI agent might discover it has a customer database server available and a Slack server, then query a customer’s record and automatically send a Slack message - all orchestrated by the agent’s reasoning. Crucially, none of this requires manual reprogramming for each combination: once servers are implemented, any MCP-aware agent can use them.

Key Benefits of MCP

MCP unlocks several important advantages for intelligent applications:

• Plug-and-play integration: With MCP, developers expose a data source once as an MCP server, and any compatible AI client can use it. There’s no need to write custom integration code for each new AI or tool. In effect, MCP servers act like modular “plugins” for AI systems. For example, pre-built MCP servers already exist for Google Drive, Slack, GitHub, Postgres, and more, which any AI can leverage immediately.
• Solves the M×N integration problem: Instead of building M×N bespoke connectors, MCP reduces it to M+N. You implement M AI clients (with MCP support) and N servers (for data sources), and any client can work with any server. This dramatically simplifies scaling. As AWS notes, MCP transforms a complex integration matrix into a straightforward setup, much like how APIs standardized web integration.
• Consistency and interoperability: MCP enforces a uniform request/response format across tools. This consistency means that when an AI agent switches from one model or vendor to another, the way it talks to tools stays the same. It also makes it much easier to debug and chain operations. In practice, the AI always “talks” JSON with MCP servers, so it doesn’t care if the backend is a cloud service, a SQL database, or an on-prem API.
• Empowers autonomous workflows: Because MCP supports discovery, context, and multi-step operations, AI agents can become far more autonomous. They are not limited to their built-in knowledge; they can actively fetch up-to-date information or perform actions. For example, an MCP-enabled agent could gather data from a CRM, process it, send an email via a communications tool, and then record results in a database — all without human intervention. This “context-aware” capability moves AI from simple Q&A towards true automation.
• Future-proof and vendor-neutral: MCP is an open standard, not tied to any one AI or cloud provider. As new AI models emerge, they can plug into existing MCP servers without rebuilding integrations. Similarly, existing AI platforms gain immediate access to any new MCP servers. This protects enterprise investments; you avoid vendor lock-in and can mix-and-match tools and models freely.
• Built-in security and governance: MCP can leverage existing identity and permission systems. Each tool call goes through the MCP server, which can enforce authentication, roles, and compliance rules. This ensures that when an AI agent accesses data, it does so in a controlled way. Permissions are handled at the protocol level, so enterprises can apply their usual access policies to MCP connections.

Together, these benefits let organizations amplify their data infrastructure for AI. As one analysis put it, MCP “replaces fragmented integrations with a simpler, more reliable single protocol for data access”, making it much easier for AI agents to fetch exactly the context they need.

Real-world use cases

MCP’s flexibility enables a wide range of intelligent workflows across industries. A few examples:

• Intelligent content generation: Imagine a marketing team that needs a product presentation. The relevant data lives in multiple systems: product specs in a PIM, customer feedback in a CRM, and market analysis in a BI tool. An MCP-enabled agent can discover these sources, query each one, and synthesize a cohesive report. Unlike a fragile script that breaks when one API changes, the agent uses the standardized MCP interface, making the process more robust.
• Automated data analysis and quality: A data steward suspects issues in supplier data. Using MCP, an AI agent can find the relevant data domains and run analysis tools on the fly. It might detect anomalies without pre-defined rules, apply business validations dynamically, and even generate reports or remediation steps. This on-the-fly intelligence - adapting to changing data and schemas - becomes practical with MCP’s unified access.
• Developer productivity: In software engineering, an AI coding assistant can use MCP to access live development resources. For instance, an agent could query a GitHub repo for code, call a test suite, or update documentation in a codebase - all through MCP servers. This turns the IDE into an “everything app” that can reach outside the editor. Early MCP adopters like Replit and Codeium are already integrating MCP to enrich code completion with real project context.
• Service orchestration: Customer service and operations can benefit too. For example, an AI agent handling support tickets might retrieve order history from an ERP, summarize the issue, and update ticket status across multiple systems automatically. Or sales teams could have a virtual assistant that pulls sales figures from databases and posts updates to Slack. These multi-step business workflows become feasible when an agent can call enterprise tools through MCP.

These scenarios (and many others) illustrate how MCP turns any AI client into a context-aware agent. By layering MCP on top of existing systems (databases, ERPs, MDM platforms, cloud services, etc.), companies transform static data APIs into dynamic, AI-ready services. Agents can not only fetch data but understand its meaning and governance, because MCP schemas carry that semantic context. The result is smarter automation: AI systems that securely tap into live data and even reason about data lineage and policies as they operate.

Conclusion

MCP provides the standard bridge that intelligent AI experiences need to access real-world data. By decoupling AI agents from custom integrations, MCP enables truly context-aware workflows across any enterprise system. Adopting this open protocol means AI applications can focus on reasoning and decision-making, while the heavy lifting of connectivity is handled seamlessly. In practice, MCP transforms powerful but isolated models into versatile collaborators that fetch, combine, and act on live business information, unlocking the next generation of AI-driven innovation.