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2026 Agentic CodingTrends ReportHow coding agents are reshapingsoftware development

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ContentsForeword: From assistance to collaboration 3Foundation trends: The tectonic shift 4Trend 1: The software development lifecyclechanges dramatically 5Capability trends: What agents can do 7Trend 2: Single agents evolve into coordinated teams 8Trend 3: Long-running agents build complete systems 9Trend 4: Human oversight scales through intelligentcollaboration 10Trend 5: Agentic coding expands to new surfacesand users 11Impact trends: What agents may change in 2026 12Trend 6: Productivity gains reshape softwaredevelopment economics 13Trend 7: Non-technical use cases expand acrossorganizations 14Trend 8: Dual-use risk requires security-firstarchitecture 15Priorities for the year ahead 162

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ForewordFrom assistance to collaborationIn 2025, coding agents moved from experimental tools to production systemsthat ship real features to real customers. Engineering teams discovered thatAI can now handle entire implementation workflows: writing tests, debuggingfailures, generating documentation, and navigating increasingly complexcodebases.In 2026, we predict these gains will extend far beyond incrementalimprovements to existing tools or models. We expect single agents to becomecoordinated teams of agents. Tasks that took hours or days may now becompleted with minimal human intervention. And engineers who, only a fewyears ago, wrote every line of code will increasingly orchestrate long-runningsystems of agents that handle implementation details so they can focus onarchitecture and strategy.Yet a critical nuance has emerged from studying how developers actually workwith AI: this transformation is fundamentally collaborative. Research from ourSocietal Impacts team reveals that while developers use AI in roughly 60% oftheir work, they report being able to "fully delegate" only 0-20% of tasks. AI servesas a constant collaborator, but using it effectively requires thoughtful set-up andprompting, active supervision, validation, and human judgment—especially forhigh-stakes work.Inspired by our own experiences working with customers, this report identifieseight trends we predict will define agentic coding in 2026. These predictionsfall into three categories: foundation trends that we believe will reshape howdevelopment work happens, capability trends that look to expand what agentscan accomplish, and impact trends that we anticipate will affect businessoutcomes and organizational structures.These predictions reflect what we're seeing with customers today, not certaintiesabout tomorrow. We offer them as a framework for thinking about the yearahead, knowing the future will surprise us.Significantly, these trends illustrate how the gap between early adopters andlate movers is widening. Organizations that figure out how to scale humanoversight without creating bottlenecks are better positioned to maintainquality while moving faster. Teams that master agent coordination acrossthe software development lifecycle today can ship features in hours insteadof days. Companies that extend agentic coding beyond engineering teamsto less technical roles stand to unlock productivity gains across their entireorganization.The patterns emerging in 2026 suggest software development is evolving towarda model where human expertise focuses on defining the problems worth solvingwhile AI handles the tactical work of implementation.Let’s dive in.3

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Foundation trends:The tectonic shift 4

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Trend 1The software development lifecyclechanges dramaticallyThe way we interact with computers is undergoing one of its most significantchanges since the graphical user interface. From machine code to assembly to Cto modern high-level languages, each abstraction layer reduced the gap betweenhuman thought and machine execution.The most recent step in this evolution was human/machine conversation.In 2025, agentic AI changed how a large swath of developers write code.2026 is poised to be the year when the systemic effects of this evolutionaryshift reconfigure the software development lifecycle and reshape softwareengineering roles.Traditional SDLC stages remain, but agent-driven implementation, automated testing, andinline documentation collapse cycle time from weeks to hours. Monitoring feeds directly backinto rapid iteration.Predictions• Evolution of abstraction: Most of the tactical work of writing, debugging, andmaintaining code shifts to AI while engineers focus on higher-level work likearchitecture, system design, and strategic decisions about what to build.• Engineering role transformation: Building software used to mean primarilywriting code, although software engineering roles always involved many otherskills. Now, being a software engineer increasingly means orchestrating agentsthat write code, evaluating their output, providing strategic direction, andensuring the system as a whole solves the right problems correctly.• Expedited onboarding to dynamic project staffing: Traditional timelinesfor onboarding to a new codebase or project will collapse from weeks tohours, changing how companies think about talent deployment and projectresourcing.The collaborative realityWhile agents handle more implementation work, the nature of this shiftreveals something important: engineers are becoming more "full-stack" in theircapabilities rather than being replaced. Our research shows engineers can nowwork effectively across frontend, backend, databases, and infrastructure—areaswhere they may have previously lacked expertise—because AI fills in knowledgegaps while humans provide oversight and direction.This capability expansion enables tighter feedback loops and faster learning.Tasks that once required weeks of cross-team coordination can become focusedworking sessions. Engineers describe using AI for tasks that are easily verifiable,5

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well-defined, or repetitive, while keeping high-level design decisions andanything requiring organizational context or "taste" for themselves.Role transformation: From implementer toorchestratorIn 2026, the value of an engineer's contributions shifts to system architecturedesign, agent coordination, quality evaluation, and strategic problemdecomposition. The primary human role in building software is orchestratingAI agents that write code, evaluating their output, providing strategic direction,and ensuring the system solves the right problems for the right stakeholders.Engineers who master orchestration can shepherd multiple features throughdevelopment simultaneously, applying their judgment across a broader scopethan individual implementation previously allowed.Onboarding revolutionIn 2025, the traditional timeline for onboarding to a new codebase or projectbegan to collapse from weeks to hours. In 2026, we anticipate organizationslearning how to use this capability to its fullest, changing how companies thinkabout talent deployment and project resourcing.One manifestation we envision is dynamic "surge" staffing. Businesses will beable to surge engineers on-demand onto tasks requiring deep codebase knowl-edge. Organizations can start staffing projects dynamically, bringing in special-ists for specific challenges and shifting resources without the traditional produc-tivity dip.Augment Code, a startup building AI-powered software developmenttools for systems like networking platforms, databases, and storageinfrastructure, flattened the learning curve for engineers joining anew codebase or project by using Claude to provide contextual codeunderstanding. One enterprise customer finished a project that their CTOhad initially estimated would take 4 to 8 months in just two weeks usingAugment Code, powered by Claude.6

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Capability trends:What agents can do 7

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Trend 2Single agents evolve into coordinated teamsWe predict that organizations in 2026 will be able to harness multiple agents actingtogether 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 thestatus of multiple concurrent agent sessions and version control workflows thathandle simultaneous agent-generated contributions.Prediction• Multi-agent systems replace single-agent workflows: Organizations adoptmulti-agent workflows that maximize performance gains through parallelreasoning across separate context windows.Fountain, a frontline workforce management platform, achieved 50%faster screening, 40% quicker onboarding, and 2x candidate conversionsusing Claude for hierarchical multi-agent orchestration.Their Fountain Copilot serves as the central orchestration agent tocoordinate specialized sub-agents for candidate screening, automateddocument generation, and sentiment analysis. This architecture enabledone logistics customer to cut the time required to fully staff a newfulfillment center from one or more weeks to less than 72 hours.Single-agent workflows process tasks sequentially through one context window. Multi-agentarchitectures use an orchestrator to coordinate specialized agents working in parallel—eachwith dedicated context—then synthesize results into integrated output.8

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Trend 3Long-running agents build complete systemsEarly 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 agentswere producing full feature sets over the course of several hours. In 2026, agentswill be able to work for days at a time, building entire applications and systemswith minimal human intervention focused on providing strategic oversight atkey decision points.Predictions• Task horizons expand from minutes to days or weeks: Agents evolve fromhandling discrete tasks that complete in minutes to working autonomously forextended periods, building and testing entire applications and systems withperiodic human checkpoints.• Agents handle the messy reality of software development: Long-runningagents plan, iterate, and refine across dozens of work sessions, adaptingto discoveries, recovering from failures, and maintaining coherent statethroughout complex projects.• Economics of software development change: When agents can workautonomously for extended periods, formerly non-viable projects becomefeasible. Technical debt that accumulated for years because no one had time toaddress it gets systematically eliminated by agents working through backlogs.• Path to market accelerates: Entrepreneurs use agents to go from ideas todeployed applications in days instead of months.At Rakuten, engineers tested Claude Code's capabilities with a complextechnical task: implement a specific activation vector extraction methodin vLLM, a massive open-source library with 12.5 million lines of code inmultiple programming languages. Claude Code finished the entire jobin seven hours of autonomous work in a single run. The implementationachieved 99.9% numerical accuracy compared to the reference method.9

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Trend 4Human oversight scales through intelligentcollaborationPerhaps the most valuable capability developments in 2026 will be agentslearning when to ask for help, rather than blindly attempting every task, andhumans stepping into the loop only when required. This isn't about removinghumans from the process—it's about making human attention count where itmatters most.Predictions• Agentic quality control becomes standard: Organizations use AI agentsto review large-scale AI-generated output, analyzing code for securityvulnerabilities, architectural consistency, and quality issues that wouldoverwhelm human capacity.• Agents learn when to ask for help: Rather than blindly attempting every task,sophisticated agents recognize situations requiring human judgment, flaggingareas of uncertainty and elevating decisions with potential business impact.• Human oversight shifts from reviewing everything to reviewing whatmatters: Teams maintain quality and velocity simultaneously by buildingintelligent systems that handle routine verification while escalating genuinelynovel situations, boundary cases, and strategic decisions for human input.The collaboration paradoxResearch from Anthropic's internal studies reveals an important pattern: whileengineers report using AI in roughly 60% of their work and achieving significantproductivity gains, they also report being able to "fully delegate" only a smallfraction of their tasks. The apparent contradiction resolves when you understandthat effective AI collaboration requires active human participation.Engineers describe developing intuitions for AI delegation over time. As modelsimprove, this is shifting quickly, but historically, they tended to delegate tasksthat are easily verifiable—where they "can relatively easily sniff-check oncorrectness"—or are low-stakes, like quick scripts to track down a bug. The moreconceptually difficult or design-dependent a task, the more likely engineers keepit for themselves or work through it collaboratively with AI rather than handingit off entirely.This pattern has important implications: even as AI capabilities expand, thehuman 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'mprimarily using AI in cases where I know what the answer should be or shouldlook like. I developed that ability by doing software engineering 'the hard way.'"At CRED, a fintech platform serving over 15 million users across India,engineers implemented Claude Code across their entire developmentlifecycle to accelerate delivery while maintaining quality standardsessential for financial services. The Claude-powered developmentsystem has doubled their execution speed—not by eliminating humaninvolvement, but by shifting developers toward higher-value work.10

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Trend 5Agentic coding expands to new surfaces and usersThe earliest wave of agentic coding focused on helping professional softwareengineers work faster within familiar environments. In 2026, agentic coding ispoised to expand into contexts and use cases that traditional development toolscould not reach, from legacy languages to new form factors that democratizeaccess beyond traditional developers.Predictions• Language barriers disappear: Support expands to less-common and legacylanguages like COBOL, Fortran, and domain-specific languages, enablingmaintenance of legacy systems and removing adoption barriers for specializeduse cases.• Coding democratizes beyond engineering: New form factors andinterfaces open up agentic coding to non-traditional developers in fieldslike cybersecurity, operations, design, and data science. Tools like Cowork,designed for non-developers to automate file and task management, signalthis shift is already underway.Everyone becomes more full-stackAnalysis of how different teams use AI reveals a consistent pattern: people useAI to augment their core expertise while expanding into adjacent domains.Security teams use it to analyze unfamiliar code. Research teams use it tobuild frontend visualizations of their data. Non-technical employees use it fordebugging network issues or performing data analysis.This expansion challenges the long-held assumption that serious developmentwork can only happen in an IDE or that only professional engineers withspecialized tools can use code to solve problems. The barrier that separates"people who code" from "people who don't" is becoming more permeable.At Legora, an AI-powered legal platform, agentic workflows are integratedthroughout their legal technology platform, demonstrating how codingagents extend into domain-specific applications."We have found Claude to be brilliant at instruction following, and atbuilding agents and agentic workflows," said Max Junestrand, CEOof Legora. The company uses Claude Code to accelerate their owndevelopment while providing agentic capabilities to lawyers who need tocreate sophisticated automations without engineering expertise.11

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Impact trends:What agents maychange in 2026 12

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Trend 6Productivity gains reshape softwaredevelopment economicsOrganizations that intelligently integrate agents into their software developmentlifecycle will see timeline compression that affects what projects are viable andhow quickly companies can respond to market opportunities.Predictions• Three multipliers drive acceleration: Agent capabilities, orchestrationimprovements, and better use of human experience compound to create step-function improvements rather than linear gains as each enables the others.• Timeline compression changes project viability: Development that oncetook weeks now takes days, making previously unviable projects feasible andenabling organizations to respond to market opportunities more quickly.• Economics of software development shift: Total cost of ownership decreasesas agents augment engineer capacity, project timelines shorten, and fastertime to value improves return on investment.Productivity through output volume, not just speedInternal research at Anthropic reveals an interesting productivity pattern:engineers report a net decrease in time spent per task category, but a muchlarger net increase in output volume. This suggests that AI enables increasedproductivity primarily through greater output—more features shipped, more bugsfixed, more experiments run—rather than simply doing the same work faster.Notably, about 27% of AI-assisted work consists of tasks that wouldn't have beendone otherwise: scaling projects, building nice-to-have tools like interactivedashboards, and exploratory work that wouldn't be cost-effective if donemanually. Engineers report fixing more "papercuts"—minor issues that improvequality of life but are typically deprioritized—because AI makes addressing themfeasible.At TELUS, a leading communications technology company, teams createdover 13,000 custom AI solutions while shipping engineering code 30percent faster. The company has saved over 500,000 hours with an averageof 40 minutes saved per AI interaction.13

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Trend 7Non-technical use cases expandacross organizationsWe anticipate that one of the most significant trends in 2026 will be steadygrowth in agentic coding used by functional and business-process teams tocreate their own solutions to problems they experience, and improvements toprocesses they use every day.Predictions• Coding capabilities democratize beyond engineering: Non-technical teamsacross sales, marketing, legal, and operations gain the ability to automateworkflows and build tools with little or no engineering intervention or codingexpertise.• Domain experts implement solutions directly: The hands-on expertswho understand problems deeply gain confidence in using agents to initiatesolutions themselves, removing the bottleneck of filing a ticket and thenwaiting for development teams.• Productivity gains extend across entire organizations: Problems notworth engineering time get solved, experimental workflows become trivial toattempt, and manual processes get automated.Zapier, a leading AI orchestration platform, has made agents accessible toall their employees. Design teams use Claude artifacts to rapidly prototypeduring customer interviews, showing design concepts in real-time thatwould normally take weeks to develop. The company achieved 89 percentAI adoption across the entire organization with 800-plus AI agentsdeployed internally.How Anthropic uses Claude CodeOur legal team reduced marketing review turnaround from two to threedays down to 24 hours by building Claude-powered workflows thatautomate repetitive tasks like contract redlining and content review. UsingClaude Code, a lawyer with no coding experience built self-service toolsthat triage issues before they hit the legal queue, freeing attorneys to focuson strategic counsel instead of tactical busywork.The result: lawyers reduced the potential for being a bottleneck and coulddevote their time to other, more pressing matters.14

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Trend 8Agentic coding improves security defenses—but also offensive usesAgentic coding is transforming security in two directions at once. As modelsbecome more powerful and better aligned, building security into productsbecomes easier. Now, any engineer can leverage AI to perform security reviews,hardening, and monitoring that previously required specialized expertise. Butthe same capabilities that help defenders are also capable of helping attackersscale their efforts.Predictions• Security knowledge becomes democratized: With improved agents, anyengineer can become a security engineer capable of delivering in-depthsecurity reviews, hardening, and monitoring. Engineers will still need toconsider security and consult with specialists, but it will become easier to buildhardened and secure applications.• Threat actors scale attacks: While agents will benefit defensive uses, theywill also benefit offensive uses too. In order to defend against this dual-usetechnology, it will become more important for engineers to build in securityfrom the start.• Agentic cyber defense systems rise: Automated agentic systems enablesecurity responses at machine speed, automating detection and response tomatch the pace of autonomous threats.The balance favors prepared organizations. Teams that use agentic tools tobake security in from the start will be better positioned to defend againstadversaries using the same technology.15

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Priorities forthe year ahead 16

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Priorities for the year aheadThese eight trends are poised to define agentic coding in 2026 all converge ona central theme: software development is shifting from an activity centered onwriting code to an activity grounded in orchestrating agents that write code—while maintaining the human judgment, oversight, and collaboration thatensures quality outcomes.The research is clear: AI is a constant collaborator, but using it effectivelyrequires active supervision and validation, especially in high-stakes work. Whilemore routine coding tasks can be delegated to AI, humans are still reviewing thecode. It's not "fully delegated" but highly collaborative. This distinction mattersfor how organizations approach AI adoption and how they think about theevolving role of engineers.For organizations planning their 2026 priorities, four areas demand immediateattention:1. Mastering multi-agent coordination to handle complexity that single-agentsystems cannot address2. Scaling human-agent oversight through AI-automated review systems thatfocus human attention where it matters most3. Extending agentic coding beyond engineering to empower domain expertsacross departments4. Embedding security architecture as a part of agentic system design from theearliest stagesOrganizations that treat agentic coding as a strategic priority in 2026 will definewhat becomes possible, while those that treat it as an incremental productivitytool will discover they are competing in a game with new rules. The key tosuccess lies in understanding that the goal isn't to remove humans from theloop—it's to make human expertise count where it matters most.17

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