Capability trends: What agents can do

Trend 2: Single agents evolve into coordinated teams

We predict that organizations in 2026 will be able to harness multiple agents acting together to handle task complexity that was difficult to imagine just a year ago.

This ability will require new skills in task decomposition, agent specialization, and coordination protocols, along with development environments that show the status of multiple concurrent agent sessions and version control workflows that handle simultaneous agent-generated contributions.

Single-agent workflows process tasks sequentially through one context window. Multi-agent architectures use an orchestrator to coordinate specialized agents working in parallel—each with dedicated context—then synthesize results into integrated output.

Prediction

• Multi-agent systems replace single-agent workflows: Organizations adopt multi-agent workflows that maximize performance gains through parallel reasoning across separate context windows.

Trend 3: Long-running agents build complete systems

Early agents handled one-shot tasks that took a few minutes at most: fix this bug, write this function, generate this test. By late 2025, increasingly adept AI agents were producing full feature sets over the course of several hours. In 2026, agents will be able to work for days at a time, building entire applications and systems with minimal human intervention focused on providing strategic oversight at key decision points.

Predictions

• Task horizons expand from minutes to days or weeks: Agents evolve from handling discrete tasks that complete in minutes to working autonomously for extended periods, building and testing entire applications and systems with periodic human checkpoints.
• Agents handle the messy reality of software development: Long-running agents plan, iterate, and refine across dozens of work sessions, adapting to discoveries, recovering from failures, and maintaining coherent state throughout complex projects.
• Economics of software development change: When agents can work autonomously for extended periods, formerly non-viable projects become feasible. Technical debt that accumulated for years because no one had time to address it gets systematically eliminated by agents working through backlogs.
• Path to market accelerates: Entrepreneurs use agents to go from ideas to deployed applications in days instead of months.

Trend 4: Human oversight scales through intelligent collaboration

Perhaps the most valuable capability developments in 2026 will be agents learning when to ask for help, rather than blindly attempting every task, and humans stepping into the loop only when required. This isn't about removing humans from the process—it's about making human attention count where it matters most.

Predictions

• Agentic quality control becomes standard: Organizations use AI agents to review large-scale AI-generated output, analyzing code for security vulnerabilities, architectural consistency, and quality issues that would overwhelm human capacity.
• Agents learn when to ask for help: Rather than blindly attempting every task, sophisticated agents recognize situations requiring human judgment, flagging areas of uncertainty and elevating decisions with potential business impact.
• Human oversight shifts from reviewing everything to reviewing what matters: Teams maintain quality and velocity simultaneously by building intelligent systems that handle routine verification while escalating genuinely novel situations, boundary cases, and strategic decisions for human input.

The collaboration paradox

Research from Anthropic's internal studies reveals an important pattern: while engineers report using AI in roughly 60% of their work and achieving significant productivity gains, they also report being able to "fully delegate" only a small fraction of their tasks. The apparent contradiction resolves when you understand that effective AI collaboration requires active human participation.

Engineers describe developing intuitions for AI delegation over time. As models improve, this is shifting quickly, but historically, they tended to delegate tasks that are easily verifiable—where they "can relatively easily sniff-check on correctness"—or are low-stakes, like quick scripts to track down a bug. The more conceptually difficult or design-dependent a task, the more likely engineers keep it for themselves or work through it collaboratively with AI rather than handing it off entirely.

This pattern has important implications: even as AI capabilities expand, the human role remains central. The shift is from writing code to reviewing, directing, and validating AI-generated code. As one of our engineers put it: "I'm primarily using AI in cases where I know what the answer should be or should look like. I developed that ability by doing software engineering 'the hard way.'"

Trend 5: Agentic coding expands to new surfaces and users

The earliest wave of agentic coding focused on helping professional software engineers work faster within familiar environments. In 2026, agentic coding is poised to expand into contexts and use cases that traditional development tools could not reach, from legacy languages to new form factors that democratize access beyond traditional developers.

Predictions

• Language barriers disappear: Support expands to less-common and legacy languages like COBOL, Fortran, and domain-specific languages, enabling maintenance of legacy systems and removing adoption barriers for specialized use cases.
• Coding democratizes beyond engineering: New form factors and interfaces open up agentic coding to non-traditional developers in fields like cybersecurity, operations, design, and data science. Tools like Cowork (opens in new tab), designed for non-developers to automate file and task management, signal this shift is already underway.

Everyone becomes more full-stack

Analysis of how different teams use AI reveals a consistent pattern: people use AI to augment their core expertise while expanding into adjacent domains. Security teams use it to analyze unfamiliar code. Research teams use it to build frontend visualizations of their data. Non-technical employees use it for debugging network issues or performing data analysis.

This expansion challenges the long-held assumption that serious development work can only happen in an IDE or that only professional engineers with specialized tools can use code to solve problems. The barrier that separates "people who code" from "people who don't" is becoming more permeable.