AI Agent Success Rates by Task Type

Coding and Software Engineering

Coding agents show the widest performance spread of any task category, with success rates heavily dependent on task complexity and the quality of specifications.

Simple code generation from clear specifications succeeds at 85-95% for leading models. Tasks like implementing a function from a docstring, converting between data formats, writing unit tests for existing code, and generating boilerplate following established patterns fall in this range. The high success rate reflects the fact that these tasks have well-defined inputs and verifiable outputs, allowing the agent to produce correct solutions reliably.

Bug fixing in existing codebases has broader variation. On SWE-Bench Verified, top systems resolve 45-55% of real GitHub issues. This number understates performance on simpler bugs and overstates it on complex ones. One-line fixes with clear error messages succeed at 70-80%. Multi-file fixes requiring deep understanding of project architecture succeed at 15-25%. The practical implication is that coding agents are already reliable for routine bug fixes but need human oversight for complex issues.

Code review and analysis tasks succeed at 60-75% when measured by the percentage of genuine issues identified. Agents consistently catch common bugs like null pointer risks, resource leaks, and boundary condition errors. They are less reliable at identifying logical errors that require understanding the business intent behind the code, architectural concerns that span multiple components, and subtle concurrency issues. Using agents as a first-pass reviewer that flags potential issues for human verification is the most effective deployment pattern.

Refactoring and migration tasks show 40-60% success rates on well-defined transformations like renaming variables, extracting functions, and updating API usage patterns. The success rate drops to 20-35% for structural refactoring that requires understanding why the code was organized in a particular way and making judgment calls about the best new structure.

Customer Support and Service

Customer support is one of the most mature categories for agent deployment, with well-established success rate data from production systems across industries.

Tier-one ticket resolution, handling routine questions with answers available in knowledge bases, shows success rates of 65-80% across production deployments. The variance depends on the quality of the knowledge base, the clarity of customer queries, and how narrowly "success" is defined. Systems that measure success by customer satisfaction scores (resolving the customer's actual need) tend to report lower rates than those that measure by query deflection (preventing the customer from reaching a human agent).

Information retrieval and lookup tasks within support contexts succeed at 80-90%. When a customer asks about their order status, account balance, or policy details, and the information is available in connected systems, agents retrieve and present it correctly at high rates. These are structured tasks with definitive correct answers, which is why the success rate is at the top of the range.

Complex issue resolution that requires multi-step troubleshooting, judgment calls about exceptions, or coordination across departments shows success rates of 25-40%. These tasks often involve ambiguity, emotional sensitivity, and scenarios that fall outside documented procedures. Most production support deployments handle these through escalation to human agents rather than attempting full autonomous resolution.

Sentiment detection and routing accuracy sits at 85-92% for well-trained systems. Agents correctly identify urgent requests, frustrated customers, and technical versus billing issues at high rates, enabling intelligent routing that gets customers to the right resource faster even when the agent cannot resolve the issue itself.

Research and Information Gathering

Research tasks test an agent's ability to find, synthesize, and present information from multiple sources, a capability that improves significantly with tool access and multi-step reasoning.

Factual research with verifiable answers succeeds at 70-85% on benchmarks like GAIA Level 1. Tasks like finding specific statistics, identifying companies in a market segment, or locating technical specifications in documentation are well-suited to agents that can search the web and process results. The main failure mode is returning outdated or incorrect information when multiple conflicting sources exist.

Comparative analysis tasks succeed at 55-70%. Comparing products, technologies, or approaches across defined criteria is something agents handle reasonably well, particularly when the criteria are specific and the information is publicly available. Quality drops when the comparison requires subjective judgment about which differences matter most for the user's specific context.

Deep research requiring synthesis across many sources shows success rates of 30-50% when measured by human evaluation of completeness and accuracy. Agents can gather relevant information efficiently but struggle with distinguishing authoritative from unreliable sources, identifying gaps in their own coverage, and synthesizing conflicting viewpoints into balanced conclusions. The output is often useful as a starting point that a human researcher refines rather than a finished product.

Market intelligence and competitive analysis tasks succeed at 40-55%. Agents can collect publicly available data about competitors, track pricing changes, and identify market trends from news and reports. They struggle with interpreting strategic implications, assessing the reliability of market size estimates, and providing the kind of nuanced competitive insight that requires industry experience.

Data Analysis and Processing

Data tasks benefit from the structured, verifiable nature of numerical work, but success rates depend heavily on the complexity of the analysis and the cleanliness of the data.

Data extraction and transformation tasks succeed at 80-92%. Parsing structured data from documents, converting between formats, cleaning and normalizing datasets, and merging data from multiple sources are tasks where agents perform consistently well. These tasks have clear specifications and verifiable outputs, and the agent can use code execution tools to validate its work.

Statistical analysis and reporting shows success rates of 60-75% for standard analyses. Generating descriptive statistics, creating standard visualizations, running basic hypothesis tests, and producing summary reports from clean datasets are within reliable agent capability. Rates drop for analyses requiring domain expertise to select appropriate methods, interpret results in context, or handle messy real-world data with missing values and outliers.

Predictive modeling and machine learning pipeline tasks succeed at 35-55% when measured by whether the agent produces a working model that meets specified performance criteria. Agents can follow established workflows for common model types but struggle with feature engineering decisions, hyperparameter optimization strategies that require understanding the problem domain, and diagnosing why a model is underperforming on specific data subsets.

SQL query generation succeeds at 75-88% for queries against well-documented schemas. Simple to moderately complex queries with joins, aggregations, and filtering are handled reliably. Complex queries involving nested subqueries, window functions, and database-specific syntax show lower rates of 50-65%. Agents that can execute queries and verify results against expected outputs achieve higher effective accuracy through iterative refinement.

Web Automation and Browser Tasks

Web automation benchmarks like WebArena reveal that browser-based tasks remain challenging, with overall success rates lower than most other task categories.

Simple web interactions like form filling, button clicking, and page navigation succeed at 55-70% in benchmark settings. The variability comes from the diversity of web interfaces, the complexity of modern single-page applications, and the difficulty of mapping natural language task descriptions to specific UI elements. Pages with clear, labeled elements are easier for agents than those relying on visual layout or implied interaction patterns.

Multi-step web workflows that span multiple pages and require maintaining state across interactions show success rates of 25-40%. An e-commerce purchase flow that requires searching for a product, selecting the right variant, adding it to cart, and completing checkout involves many individual actions, each of which can fail. Error compounding across steps drives down the overall success rate even when individual step accuracy is reasonable.

Data scraping and extraction from web pages succeeds at 65-80% when the target data has consistent structure. Agent-driven scraping handles dynamic content and JavaScript-rendered pages better than traditional scraping tools because the agent can reason about page structure and adapt to layout variations. Success rates drop for sites with aggressive anti-scraping measures or highly inconsistent page structures.

Web-based content management tasks like posting content, updating pages, and managing configurations through admin interfaces show 40-55% success rates. These tasks combine web interaction challenges with domain-specific knowledge about each platform's interface and workflow, making them doubly difficult for general-purpose agents.

Content Generation and Writing

Content generation is unique because "success" is inherently subjective, making success rate measurement less precise than for tasks with verifiable outcomes.

Structured content generation from templates or specifications succeeds at 80-90% when measured by adherence to format requirements, factual accuracy, and basic quality thresholds. Generating product descriptions from feature lists, creating social media posts from content briefs, and writing email responses from templates are tasks where agents produce consistently usable output.

Long-form content like articles, reports, and documentation shows human-rated quality scores equivalent to 60-75% "acceptable without major revision" rates. The content is generally well-organized, grammatically correct, and topically relevant, but often lacks the depth, originality, and domain expertise that distinguishes excellent content from adequate content. Human editors typically need to add specific examples, verify claims, and adjust tone to match the publication's voice.

Creative writing tasks, including fiction, marketing copy requiring emotional resonance, and persuasive writing for specific audiences, show the lowest success rates at 30-50% for "meets quality bar" assessments. These tasks require understanding audience psychology, cultural context, and stylistic nuance that current models handle inconsistently.

Translation and localization tasks succeed at 75-88% for common language pairs, measured by human evaluators assessing both accuracy and naturalness. Performance drops significantly for rare language pairs, highly specialized terminology, and content where cultural adaptation matters as much as linguistic accuracy.

Factors That Move Success Rates

Across all task categories, several factors consistently influence success rates. Task specificity matters: narrowly defined tasks with clear success criteria show higher rates than open-ended tasks with ambiguous goals. Tool access matters: agents with appropriate tools for the task outperform agents limited to text generation. Context quality matters: agents with access to relevant documentation, examples, and domain knowledge perform better than those working from general training data alone.

The model powering the agent creates a roughly 10-20 percentage point spread between the strongest and weakest mainstream options for most task categories. Agent architecture adds another 5-15 points, with multi-step, tool-equipped agents consistently outperforming single-pass approaches. Prompt engineering and task-specific tuning add another 5-10 points. The cumulative effect means that a well-engineered agent using the best available model can outperform a poorly configured agent on a weaker model by 30-40 percentage points on the same tasks.