"""Agent Adoption Surveys and Real-World Developer AI Data Source: LangChain, McKinsey, PwC surveys; GitHub, JetBrains, DX DevCycle; academic studies; Omdia, BCC Research, MarketsandMarkets, Grand View Research. Retrieved: June 2026 IMPORTANT: This module prioritizes REAL-WORLD data over lab benchmarks. Benchmark scores are included only with heavy disclaimers. """ from __future__ import annotations from typing import Any # --------------------------------------------------------------------------- # Module metadata # --------------------------------------------------------------------------- MODULE_NAME: str = "agent_adoption" MODULE_VERSION: str = "1.0.0" DATA_RETRIEVED: str = "June 2026" MODULE_DISCLAIMER: str = ( "This module prioritizes REAL-WORLD data over lab benchmarks. " "Benchmark scores are included only with heavy disclaimers." ) # --------------------------------------------------------------------------- # Dataset J: Agent Adoption Surveys # --------------------------------------------------------------------------- agent_survey_data: dict[str, dict[str, Any]] = { # Source: LangChain State of Agent Engineering (Nov-Dec 2025) # 1,340 respondents surveyed on agent engineering practices. "langchain_2025": { "production": 57.3, # % deploying agents in production "observability_implemented": 89, # % with observability in place "full_tracing_prod": 71.5, # % with full tracing in production "multi_model_deployments": 75, # % using multi-model deployments "barrier_quality_percent": 32, # % citing quality as top barrier "barrier_security_enterprise_percent": 24.9, # % citing security for enterprise "barrier_latency_percent": 20, # % citing latency as barrier "sample_size": 1340, "date": "2025-11 to 2025-12", "source": "LangChain State of Agent Engineering", }, # Source: McKinsey State of AI 2025 (Nov 2025) # 1,993 executives surveyed on AI adoption and scaling. "mckinsey_2025": { "overall_ai_adoption": 88, # % of respondents adopting AI "agentic_ai_scaling": 23, # % scaling agentic AI "agentic_ai_experimenting": 39, # % experimenting with agentic AI "in_experimentation_stage": 32, # % in experimentation stage "in_piloting_stage": 30, # % in piloting stage "ai_scaling_enterprise_wide": 31, # % scaling enterprise-wide "expect_workforce_decrease": 32, # % expecting workforce decrease "expect_no_change": 43, # % expecting no workforce change "expect_workforce_increase": 13, # % expecting workforce increase "sample_size": 1993, "date": "2025-11", "source": "McKinsey State of AI 2025", }, # Source: PwC AI Agent Survey (Apr 2025) # 308 business leaders surveyed on AI agent adoption. "pwc_2025": { "plan_increase_ai_budgets": 88, # % planning to increase AI budgets "ai_agents_already_adopted": 79, # % already adopting AI agents "measurable_productivity_value": 66, # % reporting measurable productivity value "cost_savings_reported": 57, # % reporting cost savings "faster_decision_making": 55, # % experiencing faster decision making "improved_customer_experience": 54, # % reporting improved customer experience "agents_reshape_workplace_more_than_internet": 75, # % saying agents will reshape workplace more than the internet "sample_size": 308, "date": "2025-04", "source": "PwC AI Agent Survey", }, } # --------------------------------------------------------------------------- # Agent Market Forecasts # --------------------------------------------------------------------------- # Sources: Omdia, BCC Research, MarketsandMarkets, Grand View Research. # All figures in USD billions unless noted. agent_market_forecasts: list[dict[str, Any]] = [ { "source": "Omdia", "category": "Enterprise Agentic AI", "year_2025_billions": 1.5, "year_2030_billions": 41.8, "cagr_percent": 175, "date": "2025-09", }, { "source": "BCC Research", "category": "AI Agents", "year_2025_billions": 5.7, "year_2030_billions": 48.3, "cagr_percent": 43.3, }, { "source": "MarketsandMarkets", "year_2025_billions": 7.84, "year_2030_billions": 52.62, "cagr_percent": 46.3, }, { "source": "Grand View Research", "year_2025_billions": 7.63, "year_2033_billions": 182.97, "cagr_percent": 49.6, }, ] # --------------------------------------------------------------------------- # GitHub Framework Stats (qualitative — no exact star counts available) # --------------------------------------------------------------------------- github_framework_stats: dict[str, Any] = { "CrewAI": { "position": "top agent framework", "notes": "rapidly growing within LangChain ecosystem", }, "LangGraph": { "position": "top agent framework", "notes": "rapidly growing within LangChain ecosystem", }, "AutoGen": { "position": "top agent framework", "notes": "Microsoft-backed multi-agent framework", }, # Market share of paid AI coding tools "market_share_copilot": 42, # % of paid AI coding tools "market_share_cursor": 18, "market_share_amazon_q": 11, } # --------------------------------------------------------------------------- # Dataset K: Real-World Developer AI Data # --------------------------------------------------------------------------- developer_ai_adoption: list[dict[str, Any]] = [ { "source": "GitHub", "metric": "all_time_copilot_users", "value": 20_000_000, "date": "2025-07", "note": "includes free/student", }, { "source": "GitHub", "metric": "paid_copilot_subscribers", "value": 4_700_000, "date": "2026-01", }, { "source": "GitHub", "metric": "fortune_100_adoption_percent", "value": 90, "date": "2025", }, { "source": "JetBrains 2025", "metric": "regular_ai_usage_percent", "value": 85, "date": "2025", }, { "source": "JetBrains 2025", "metric": "rely_on_coding_assistant_percent", "value": 62, "date": "2025", }, { "source": "Stack Overflow 2025", "metric": "use_or_plan_ai_tools_percent", "value": 84, "date": "2025", }, { "source": "Stack Overflow 2025", "metric": "professional_devs_using_ai_daily", "value": 51, "date": "2025", }, { "source": "DX DevCycle Q4 2025", "metric": "ai_adoption_in_active_repos", "value": 91, "date": "2025-Q4", }, { "source": "DX DevCycle Q4 2025", "metric": "merged_code_ai_authored_percent", "value": 22, "date": "2025-Q4", }, ] code_acceptance_rates: list[dict[str, Any]] = [ { "tool": "GitHub Copilot", "acceptance_rate_percent": 30, "code_retention_percent": 88, "source": "GitHub/Microsoft study", "date": "2025", }, { "tool": "GitHub Copilot (heavy users)", "acceptance_rate_percent": 29.73, "source": "GitHub/Microsoft study", "date": "2025", }, ] real_world_productivity_impact: list[dict[str, Any]] = [ { "company": "Accenture RCT", "system": "GitHub Copilot", "metric": "PRs_per_developer_increase", "value_percent": 8.69, "note": "randomized controlled trial", "source": "Accenture study", "date": "2025", }, { "company": "Accenture RCT", "system": "GitHub Copilot", "metric": "PR_merge_rate_increase", "value_percent": 11, "source": "Accenture study", }, { "company": "Accenture RCT", "system": "GitHub Copilot", "metric": "successful_builds_increase", "value_percent": 84, "source": "Accenture study", }, { "company": "Google", "metric": "code_now_ai_assisted_percent", "value": 21, "date": "2025", "source": "Google internal", }, { "company": "Microsoft Research", "metric": "productivity_improvement_range", "value": "20-45%", "source": "Microsoft Research 2024-2025", }, ] code_quality_in_production: list[dict[str, Any]] = [ { "finding": "29.1% of Python AI-generated code contains security weaknesses", "source": "Academic study (733 code snippets)", "confidence": "HIGH", "cwe_categories": 43, }, { "finding": "24.2% of JavaScript AI-generated code has security weaknesses", "source": "Same academic study", "confidence": "HIGH", }, { "finding": "48% of AI-generated code contains potential security vulnerabilities", "source": "Multiple industry analyses", "confidence": "MEDIUM", }, { "finding": "40% of Copilot-generated programs flagged for insecure code", "source": "GitHub Copilot research", "confidence": "HIGH", }, { "finding": "AI-coauthored PRs have ~1.7x more issues", "source": "CodeRabbit Dec 2025 / DX DevCycle", "confidence": "HIGH", }, { "finding": "6.4% secret leakage rate in Copilot repos (40% higher than 4.6% baseline)", "source": "Academic security research", "confidence": "MEDIUM", }, { "finding": "Google DORA 2024: AI use causes 7.2% drop in delivery stability", "source": "Google DORA report", "confidence": "HIGH", }, ] failure_modes: list[dict[str, Any]] = [ { "category": "pilot_to_production_failure", "rate_percent": 72, "source": "McKinsey State of AI 2025", "confidence": "HIGH", "note": "72% of AI initiatives fail to reach production", }, { "category": "ai_pilots_zero_roi", "rate_percent": 95, "source": "MIT Media Lab 2025", "confidence": "HIGH", "note": "95% of corporate AI pilots deliver zero measurable return", }, { "category": "companies_abandoned_ai", "rate_percent": 42, "source": "S&P Global 2025", "confidence": "HIGH", "note": "42% of companies abandoned most AI initiatives in 2025", }, { "category": "projects_fail_to_profit", "rate_percent": 48, "source": "Microsoft 2025 market study", "confidence": "MEDIUM", "note": "48% of IT leaders said AI projects were NOT profitable", }, { "category": "ai_projects_overall_fail", "rate_percent": 80, "source": "RAND Corporation 2025", "confidence": "MEDIUM", "note": "Over 80% of AI projects fail — twice non-AI rate", }, ] developer_sentiment: list[dict[str, Any]] = [ { "survey": "Stack Overflow 2025", "finding": "84% use or plan to use AI tools", "sample_size": "~70,000", }, { "survey": "JetBrains 2025", "finding": "85% regular AI usage, 62% rely on at least one coding assistant", "sample_size": "~30,000", }, { "survey": "Accenture RCT", "finding": "90% felt more fulfilled, 91% enjoyed coding more with Copilot", "sample_size": "RCT participants", }, { "survey": "Various", "finding": "71% of developers do NOT merge AI code without manual review", "confidence": "MEDIUM", }, { "survey": "Various", "finding": "97% use AI tools before company policies allow (shadow IT)", "confidence": "MEDIUM", }, ] # --------------------------------------------------------------------------- # Benchmark Scores (HEAVY DISCLAIMER APPLIES) # --------------------------------------------------------------------------- # # !!! LAB BENCHMARK ONLY — Does not measure production capability, # !!! debugging, architecture, or code quality. # !!! Real-world performance may differ significantly. # !!! These numbers should NOT be used as proxies for real-world coding ability. # benchmark_scores_with_disclaimer: list[dict[str, Any]] = [ { "model": "Claude Opus 4.5", "swe_bench_verified_percent": 80.9, "disclaimer": ( "LAB BENCHMARK ONLY — Does not measure production capability, " "debugging, architecture, or code quality. " "Real-world performance may differ significantly." ), "date": "2025", }, { "model": "Claude Mythos Preview", "swe_bench_verified_percent": 93.9, "disclaimer": ( "LAB BENCHMARK ONLY — Does not measure production capability, " "debugging, architecture, or code quality. " "Real-world performance may differ significantly." ), "date": "2025", }, ]