What Not to Build: How Smart Technical Leaders Are Approaching Agentic AI in 2026

Every few years, a new technology emerges that tempts every engineering leader to build everything themselves. Ten years ago, it was microservices. Five years ago, it was data pipelines. Today, it is agentic AI.

The pattern is always the same. A team gets excited about the possibilities. They start writing custom orchestration layers, building their own planners, and stitching together brittle "glue code" to make agents work. It feels like the right thing to do. It feels like owning your destiny.

But if you talk to the technical leaders who have been through this cycle before, a different message emerges. They will tell you that the smartest thing you can do right now is not to build more. It is to build less.

Here is what developers, CTOs, and technical founders need to understand about building with agentic AI in 2026, and what you should stop building yourself.

The Glue Code Trap

For the past year, most organizations building agentic systems have followed a similar path. They started with large language models, recognized their limitations, and began writing custom code to fill the gaps.

This "glue code" handled planning, context management, memory, and orchestration. It was manual, brittle, and required constant maintenance. But it worked, at least well enough for pilots and proofs of concept.

According to Sreenivas Vemulapalli, senior vice president and chief architect of enterprise AI at digital consultancy Bridgenext, this approach is becoming obsolete. He predicts that in 2026, many enterprises will view this manual orchestration as a waste of resources. Vendors are creating new "agentic primitives", standardized building blocks for agentic systems, and embedding them directly into AI platforms and enterprise software suites .

The strategic value for your organization no longer lies in building the agent's "brain" or the plumbing that connects it. It lies in something else entirely.

Derek Ashmore, agentic AI enablement principal at Asperitas, puts it bluntly: "The smart move is to treat low-level agent orchestration as a temporary advantage, not a permanent asset" . He estimates that between 10 and 20 percent of leading firms are already standing up internal agent platforms because off-the-shelf tools don't yet provide the reliability and control they need. But they are doing so with their eyes open, knowing that much of this work will soon be commoditized.

The advice from those who have been through this before is consistent: do not overinvest in bespoke planners and routers that your cloud provider will give you in a year .

The Commoditization Wave

If you are a technical leader, you have seen this movie before. In the early days of cloud computing, everyone built their own deployment scripts. Then came configuration management tools. Then came platform-as-a-service. Today, you would never build your own deployment pipeline from scratch.

The same thing is happening with agentic AI.

The components that today require custom engineering, standardized tool interfaces, shared memory and state layers for agents, policy and guardrail frameworks, evaluation harnesses for measuring agent behavior, are rapidly becoming commodity offerings . The major cloud providers and AI platforms are racing to productize these primitives.

This does not mean your work is wasted. It means you need to be strategic about where you place your bets.

According to Ashmore, the organizations that will succeed are those that put their money where the value will persist, regardless of which agent framework ultimately wins. He recommends focusing your investments on :

High-quality domain knowledge and ontologies that encode how your business actually works Golden data sets and evaluation suites that let you measure agent performance against real requirements Security and governance policies that keep agents operating safely within your environment Integration into your existing software development lifecycle and security operations workflows Metrics that help you decide whether an agentic system is safe and cost-effective enough to trust in production

The agent engine itself will become a replaceable component. Use it now to learn what works, but architect your stack so you can swap in vendor innovations as they mature. Your real differentiation lives in the domain models, policies, and evaluation data that no platform vendor can ship for you .

The Production Reality

While the architectural debate continues, the data on production deployments tells a sobering story.

A new study from Dynatrace, surveying 919 senior leaders responsible for agentic AI implementation, reveals that organizations are hitting real barriers as they try to scale . The top blockers are not technical in the way you might expect. They are about trust and control.

Fifty-two percent of respondents cite security, privacy, and compliance concerns as their primary barrier. Fifty-one percent point to technical challenges in managing and monitoring agents at scale. Forty-five percent struggle with defining when agents should act autonomously versus when they require human approval. And 42 percent lack the real-time visibility needed to trace and troubleshoot agent behavior .

Organizations are not struggling with ideas. They are struggling with control.

The same study found that 69 percent of agentic AI decisions are still verified by a human. Only 13 percent of organizations rely exclusively on fully autonomous agents. The rest combine supervised and autonomous models, with leaders expecting a 60/40 human-in-the-loop balance for business applications and a 50/50 balance for IT and customer support functions over the long term .

This has profound implications for how you build. If you are designing systems that require humans to verify 69 percent of decisions, you need observability. You need logging. You need audit trails. You need the ability to explain why an agent took a particular action. These are not features you can bolt on after the fact. They must be designed in from the start.

The Integration Challenge

There is another layer to this story that technical leaders cannot afford to ignore. It is about connectivity.

The MuleSoft Connectivity Benchmark Report, surveying 1,050 IT leaders globally, found that the average organization now manages 957 applications . The number rises to 1,057 for organizations that have fully embraced agentic transformation. But here is the critical number: on average, only 27 percent of these applications are connected.

Even among organizations that have achieved agentic transformation, only 32 percent of their applications are connected .

This lack of connectivity creates a fundamental barrier. Eighty-two percent of IT leaders cite data integration as one of the biggest challenges their organization faces when using AI. Eighty-six percent agree that without proper integration, AI agents can introduce more complexity rather than value .

Currently, half of all AI agents operate in silos rather than as part of a cohesive multi-agent system . If your agents cannot talk to your systems, they cannot act on your behalf.

The report also reveals that 94 percent of IT leaders state that AI agents will require IT architecture to be more API-driven. Yet 27 percent of all APIs remain ungoverned within organizations, and only 54 percent have a centralized governance framework with formal policies for their agentic capabilities .

The Standards Landscape

One reason for the current fragmentation is that the standards are still evolving. The MuleSoft report found that 40 percent of respondents are using Agent-to-Agent (A2A) protocols, and 39 percent are using the Model Context Protocol (MCP) . But 68 percent of IT leaders find it challenging to stay current with emerging standards.

This is where the "build versus buy" decision gets complicated. If you build your own agent communication layer today, you may have to rip it out in twelve months when the industry converges on a standard. If you wait for standards to mature, you may fall behind competitors who are already delivering value.

The pragmatic path is to build in a way that anticipates change. Use abstractions that can be swapped out. Keep your business logic separate from your communication protocols. And stay close to the standards conversations happening in organizations like IETF and ETSI .

Where to Place Your Bets

If you are a developer, CTO, or technical founder trying to navigate this landscape, the path forward requires clear priorities.

First, stop building agent plumbing. The planners, routers, and orchestrators you are writing today will be commodities tomorrow. Use open source frameworks to learn, but do not bet your differentiation on custom orchestration code.

Second, invest in what makes your business unique. Your domain models, your data, your evaluation criteria, your security policies, these are the assets that no vendor can replicate. Structure them so agents can use them.

Third, design for observability from day one. You cannot trust what you cannot see. Build logging, tracing, and audit capabilities into every agent you deploy. The organizations that succeed at scale will be those that can explain why their agents did what they did.

Fourth, treat connectivity as infrastructure. If your systems are not connected, your agents cannot act. Prioritize API governance, data integration, and the plumbing that makes agentic workflows possible.

Fifth, plan for human oversight. The data is clear: fully autonomous agents are rare. Most organizations are building hybrid systems where humans verify decisions, handle exceptions, and provide strategic direction. Design your workflows to support this reality.

The next few years will be the most transformative in software development since the cloud . The organizations that succeed will not be those that build the most sophisticated agent frameworks. They will be those that understand what to build, and what to leave to the platforms.