As this year comes to a close, many experts have begun to look ahead to next year. Here are several predictions for trends in AI in 2026.
Ariel Katz, CEO of Sisense
From agent hype to outcome accountability
2025 was the year agents exploded; 2026 is the year enterprises demand proof they actually work. After millions spent on tokens, tools, and experiments that never reached production, companies shift from buying AI components to buying measurable business outcomes. The winners will offer outcome-as-a-service – owning the workflow, the integration, the semantics, and the last mile – because customers won’t pay for agents. They’ll pay for certainty.
Andrew Sellers, VP of technology strategy and enablement at Confluent
2026 will see new protocols for multi-agent coordination and metadata exchange
Two critical standards are likely to emerge in 2026 as AI operations become autonomous. First, as single-agent systems evolve into complex multi-agent teams, the industry needs an orchestration protocol to manage how agents work together. Current frameworks handle individual agents well, but coordinating multiple agents — determining which agent leads, which executes tasks, and how they share results — requires a standardized approach to avoid custom coding for each implementation.
Second, we need a comprehensive metadata standard to solve the structured data problem. Current metadata catalogs, like AWS’s Glue, Snowflake’s Polaris, and Databricks Unity, lack conventions for transferring metadata between platforms. Without this, data loses critical contextual information each time it moves between systems, undermining the governance agents require for trustworthy decision-making.
As the industry continues to build out the technologies to enable operational agentic AI, it’s likely we’ll see these new protocols emerge sooner rather than later.
Vikas Mathur, chief product officer at MariaDB
The era of the purely human-built application is officially over
Up to now, AI was an add-on, a feature we used to assist. In the coming year, we will witness the critical pivot where enterprise applications become ‘agentic by default,’ delegating core, multi-step logic and autonomous action to AI agents. This is the single biggest architectural shift in software development since the move to the cloud, and it means the data infrastructure must evolve from passive storage to a proactive, reasoning partner – aka databases becomes agentic as well. The success of the agentic era hinges entirely on the database’s ability to interact with application agents providing contextually grounded data with ultra-low latency and very high throughout.
Tyler Akidau, CTO of Redpanda
In 2026, enterprises will wake up to the governance crisis of AI agents
As fleets of autonomous agents proliferate across data systems, CTOs and CIOs will realize that their biggest bottleneck isn’t model performance — it’s governance. They’ll discover that traditional IAM and RBAC tools can’t keep pace with short-lived, dynamic agents acting across hundreds of services. Most organizations won’t have the time or resources to build bespoke control planes, accelerating adoption of open frameworks and shared standards like MCP and A2A.
Anahita Tafvizi, chief data and analytics officer at Snowflake
AI quality control will become a core enterprise function
As the hype around building AI agents gives way to operational reality, the center of gravity will shift from creation to validation. By 2026, enterprises will stand up dedicated AI Quality Control (QC) functions — think of them as internal “AI Councils” — to ensure trust, consistency, and accountability.
The old adage “garbage in, garbage out” now carries higher stakes. Poor data quality won’t just skew dashboards; it will drive flawed decisions, erode customer trust, and hit revenue. QC teams will set the launch gates for AI agents, defining rigorous criteria for accuracy, consistency, and alignment with business goals.
Anyone can ship an AI tool with a slick UI. The winners will be those who master the hard craft of making their AI correct. That’s why AI Quality Control is poised to emerge as a core business function — embedding governance into the heart of enterprise AI.
Kat Gaines, senior manager of developer relations at PagerDuty
The AI incident will become a distinct category
Organizations will start to treat AI system failures as their own incident classification, separate from traditional infrastructure or application issues. We’ll see the emergence of specialized runbooks for AI model drift, hallucination events, and security risks like prompt injection attacks. These incidents will require even more cross-functional than usual response teams across every part of a business, forcing a rethinking of on-call rotations and availability of subject matter experts in ML engineering, data scientists, and even parts of the business that may not be used to incident response. Companies will start measuring “AI reliability” as a distinct metric alongside traditional SLOs.
Tamar Bercovici, VP of engineering at Box
Methods for measuring AI success will shift
As the debate continues between developers who see AI as a huge accelerant and those who think it’s mostly creating “AI slop,” I think 2026 will be a real turning point in how we define productivity,” said Tamar Bercovici, VP of Engineering at Box. “Instead of measuring output by how much code gets written, teams will be evaluated on how effectively they use AI to improve the quality and impact of their work. I wouldn’t be surprised if we start seeing new roles emerge inside companies for people dedicated to helping developers use AI coding tools the right way so they can maintain high-quality code without sacrificing speed.
Keith Kuchler, chief product and technology officer at Sumo Logic
The rise of the agent economy
“The proliferation of AI agents will spark the creation of a new “Agent Economy,” where intelligent systems compete not just on price or capability, but on trust, transparency, ability and context. As agent marketplaces emerge, businesses and individual will need to evaluate AI agents like job candidates. They will interview, validate, and select the AI agents based on reliability and data integrity as well as ‘salary’/cost similar to the behavioral methods used when evaluating human talent. This will redefine how digital labor is valued and will introduce new methods for identity verification, intellectual property protection, and ethical data use. In 2026, managing the safe flow of knowledge and context-specific intelligence will become a core competitive differentiator, as companies navigate the tension between open innovation and uncontrolled data exposure.”
Paul Aubrey, director of product management at NetApp Instaclustr
Composable intelligence will replace monolithic AI
The next frontier in AI/ML isn’t about building bigger models, it’s about making smaller ones work together. The rise of Model Context Protocol (MCP) and agentic frameworks will turn AI into a composable ecosystem of reusable, discoverable micro-agents. Organizations will deploy fleets of ML models, each powering specialized classification, prediction, and recommendation tasks, each behind MCP endpoints that plug directly into the agent mesh.
Manvinder Singh, VP of AI product management at Redis
The rise of context engines
By 2026, as AI agents become deeply embedded in software and business systems, their biggest bottleneck won’t be reasoning—it will be serving them the right context at the right time. Developers are realizing that stitching together vector databases, long-term memory storage, session stores, SQL databases, and API caches creates a fragile patchwork of solutions. The next evolution will be unified “context engines”—platforms that can store, index, and serve all forms of data through a single abstraction layer. These systems will merge structured and unstructured retrieval, manage both persistent and ephemeral memory, and dynamically route information across diverse sources. This unification will replace fragmented architectures, reduce latency, simplify development, and enable AI agents to operate with fluid, on-demand intelligence across all data modalities.
Dr. Marelene Wolfgruber, Document AI lead and computational linguist at ABBYY
Context becomes the currency of collaboration
Across critical thinking, domain-specific models, MCP-driven interoperability, and vibe coding, one theme dominates: context is everything.
By 2026, the most successful systems will combine human insight with AI precision, passing context fluidly between tools, agents, and people. Developers will move from building isolated features to designing context-aware workflows—where preferences, constraints, history, and intent persist across the stack.
This shift also defines the new developer literacy: prompting with precision, relying on grounded AI outputs, and designing systems where humans stay in the loop for edge cases, ethics, and strategic decisions.
Read our other 2026 predictions: