As a software or solution provider planning for 2026, one of your top priorities must be educating yourself about AI trends and understanding where new opportunities exist in your market. One of the biggest demands is for solutions that make agentic AI work effectively for your clients’ use cases. The potential of agentic AI, for both you and your clients, is massive.
Grand View Research projects that the global AI agents market, estimated at $7.63 billion in 2025, will grow at a phenomenal 49.6% CAGR from 2026 to 2033 to reach $182.97 billion These autonomous systems can execute complex tasks by reasoning, planning and interacting with their environments, often with minimal human input and, in effect, act as digital employees. Depending on your market or niche, you’ll find an eager audience for agentic AI solutions that overcome long-time challenges like streamlining payment flows, reducing losses from fraud, or bringing real-time visibility and control to supply chain management.
While 2025 will go down in history as the year agentic AI sent shock waves through virtually all industry sectors, 2026 will be the year that IT leaders focus on building the right environments for agents’ effective and reliable operation. In 2026, businesses will seek ways to optimize agentic AI performance with improved data quality and availability where context is king. Software and solution providers that rise to the top of their markets will break down data silos. Data streaming—spanning the operational and analytical data estates—allowsAI agents to search, decide, and purchase autonomously, and help businesses finally see the return they expect from AI implementations.
Data-Streaming-Dependent 2026 AI Trends
Research from Confluent’s 2025 Data Streaming Report confirms that 90 percent of IT leaders are increasing their investments in data streaming, recognizing that it is the enabler of these 2026 AI trends.
1. Focus on Context Engineering
Few workflows that lead to the outcomes your clients are looking for are simple and one-dimensional. You need to address complex and long series of tasks in context to achieve the right outcomes without overloading AI models with content.
Context engineering gives AI systems the right data at the right time to make the best possible decisions. Like briefing a human expert, an AI agent performs best when it has full context: the history of a situation, relevant documents, and live updates on what’s happening right now. In traditional software, data prep happened offline: cleaned, batched, and delivered later. But in agentic AI, data preparation is continuous and real time. The pipeline is the application. Delivering too much or outdated data can slow performance, increase cost, and degrade accuracy. The challenge for modern enterprises is to build a continuous AI context pipeline that curates and serves only the most relevant, up-to-date information—instantly and reliably. Fresh context allows AI agents to be the eyes and ears of the business, continuously monitoring and taking action at the right time.
2. Greater Investments into Data Governance
Data governance ensures that vital data is trustworthy, compliant, auditable, and secure. Software and solution providers must identify weak links in data chains that rely on institutional knowledge or siloed documentation and replace them with tools that allow them to orchestrate multi-agent systems.
Data governance is also necessary for transparency. Your clients’ teams want to know that the AI agent’s recommendations or actions are based on traceable and trustworthy information. You need to give your users a way to answer the question, “What data was used to make this decision?” Providing transparency builds trust, which can lead to higher adoption. It’s also necessary for compliance in highly regulated industries, like financial services. Delivering a solution that provides data transparency requires auditable records, governing access, ensuring data quality, and reducing the complexity of data flows.
3. Using Durable Execution Engines for Reliability
Another AI trend to align with in 2026 is leveraging durable execution engines, like Temporal or Cadence. These tools bring fault tolerance to AI. They allow you to write complex applications that run to completion, even if failures, crashes, or network issues occur. Durable execution engines handle these events logically, starting new processes and restoring workflows to the last saved checkpoint. Early adopters will see a definite advantage as they take resilient solutions to market that their clients can depend on.
4. Strengthening Defense Against Cyberattacks and Fraud
While your clients are exploring how they can use AI to enhance their processes, the hard truth is that criminal organizations are doing the same thing. Fortunately, data streaming can help your clients fight back. For example, in the banking industry, data streaming enables detection and blocking of suspicious activity to prevent losses.
Your clients will appreciate that you take a holistic approach when providing them with AI solutions that secure data, protect systems, and prevent manipulation.
2026 Will Bring AI into Focus
In the coming months, software and solution providers should explore how to deliver enterprise-ready data streaming and unlock prevalent AI trends at scale. Staying focused on simplification, laying the groundwork for a practical future-proof data architecture, and enabling governed, secured and trusted data streams to give AI agents and applications the right data whenever necessary to address any problem at hand.
To learn more about creating scalable, manageable data flows, download Confluent’s 2026 Predictions Report.
