01 · Core thesis **Agent talent in this report:** People whose primary responsibilities directly involve building, operating, researching, evaluating, productizing, or technically deploying Agent systems. The counted JDs and professionals are strongly Agent-related; the displayed group names omit the repeated Agent prefix. Agent systems use models to plan steps, call tools, and complete tasks. **Ten job groups:** Application Engineering; Research / Model Behavior; Runtime / Platform; Deployment Engineering; Product / Design; Post-training / Robustness; Evals / Quality; Orchestration / Workflow; Solutions Architecture; and Safety / Governance. ## Agent talent is several job markets sharing a production foundation This report is based on 259 visible Agent JDs and 513 observed current Agent professionals across 32 U.S. companies. The same ten job groups connect hiring demand with talent stock, making company priorities directly comparable. 513 Observed current Agent professionals Publicly visible lower bound 259 Active Agent JDs Visible hiring demand 10 Unified job groups 259 JDs · 513 people 53.2% Talent share held by the top two companies 273 / 513 _01_ The shared Agent foundation is integration, evaluation, and reliable operation Reliable operation, Continuous evaluation, and Real-system integration appear in 87.3%, 73.4%, and 69.5% of JDs; LangChain / LangGraph appears in only 9.3%. _02_ Application Engineering is largest, but each company's primary gap is different Application Engineering has 77 JDs across 18 companies. Yet 11 of Google's 22 JDs are Deployment Engineering, 8 of Salesforce's 27 are Orchestration / Workflow, and OpenAI also posts 6 Post-training / Robustness and 5 Safety / Governance roles. _03_ Talent reservoirs and the most aggressive hirers are different company sets Microsoft and Salesforce hold 273 / 513 observed current professionals. Scale AI and OpenAI post 68 JDs combined but have only 18 observed current professionals, revealing a clear mismatch between hiring demand and visible talent stock. _04_ The main talent sources extend beyond AI labs into cloud, consulting, and enterprise software Amazon / AWS is the largest visible source with 21 people; Bain feeds 6 into Decagon, while Slack and MuleSoft together feed 8 into Salesforce. Different Agent roles draw from different source ecosystems. 02 · Company bottlenecks ## The same Agent label hides very different hiring needs The same Agent label hides different needs: OpenAI centers on safe integration into real systems, Anthropic on model-behavior evaluation, Scale AI on evaluation pipelines, Salesforce on workflow validation, and ServiceNow on state-layer operation. They share an Agent label but compete for different capability stacks. OpenAI **33 JD** ### Safe system integration Real-system integration covers 33 of 33 JDs, and Safety / permissions covers 32 of 33. OpenAI's outlier is that almost every Agent JD touches real-system boundaries, with demand moving beyond demo-building. Real-system integration 100.0%33 / 33 Reliable operation 100.0%33 / 33 Safety / permissions 97.0%32 / 33 Continuous evaluation 57.6%19 / 33 Agent orchestration 33.3%11 / 33 Data source: Metix AI Anthropic **16 JD** ### Model-behavior evaluation Continuous evaluation covers 12 of 16 JDs, above Real-system integration at 6 of 16. This is the clearest split from OpenAI: Anthropic looks more like an evaluation, environment, and model-behavior formation. Real-system integration 37.5%6 / 16 Reliable operation 87.5%14 / 16 Safety / permissions 68.8%11 / 16 Continuous evaluation 75.0%12 / 16 Memory / state 12.5%2 / 16 Data source: Metix AI Scale AI **35 JD** ### Evaluation factory Continuous evaluation covers 35 of 35 JDs, the only saturated capability among the six; Agent orchestration also covers 19 of 35, pointing to scalable evaluation and feedback pipelines. Real-system integration 80.0%28 / 35 Reliable operation 85.7%30 / 35 Safety / permissions 57.1%20 / 35 Continuous evaluation 100.0%35 / 35 Agent orchestration 54.3%19 / 35 Data source: Metix AI Google **22 JD** ### Cloud/product orchestration Google's Agent orchestration covers 12 of 22 JDs, above the 42.1% market baseline; Real-system integration is only 12 of 22, unlike OpenAI's saturation. Real-system integration 54.5%12 / 22 Reliable operation 86.4%19 / 22 Safety / permissions 63.6%14 / 22 Continuous evaluation 68.2%15 / 22 Agent orchestration 54.5%12 / 22 Data source: Metix AI Salesforce **27 JD** ### Workflow validation Continuous evaluation covers 26 of 27 JDs, and Real-system integration covers 24 of 27. Salesforce concentrates demand on connecting enterprise workflows and measuring them continuously. Real-system integration 88.9%24 / 27 Reliable operation 92.6%25 / 27 Safety / permissions 48.1%13 / 27 Continuous evaluation 96.3%26 / 27 Agent orchestration 48.1%13 / 27 Memory / state 3.7%1 / 27 Data source: Metix AI ServiceNow **27 JD** ### State-layer operation Memory / state covers 6 of 27 JDs, 3.4 times the 6.6% market baseline. ServiceNow's difference looks closer to long-running workflows, ticket state, and platform operation. Real-system integration 44.4%12 / 27 Reliable operation 88.9%24 / 27 Safety / permissions 40.7%11 / 27 Continuous evaluation 44.4%12 / 27 Agent orchestration 33.3%9 / 27 Memory / state 22.2%6 / 27 Data source: Metix AI Vertical companies reveal narrower capability gaps LangChain ### State-layer company Only 17 of 259 market JDs mention Memory / state; LangChain reaches 5 of 6, while Agent orchestration reaches 6 of 6. **5 / 6** Memory / state**6 / 6** Agent orchestration Snowflake ### Data-system integration All 6 Snowflake JDs mention Real-system integration, Continuous evaluation, and Reliable operation, concentrating demand on connecting Agents to data systems and keeping them stable. **6 / 6** Real-system integration**6 / 6** Continuous evaluation Harvey ### Legal-workflow deployment All 6 JDs saturate Real-system integration, Continuous evaluation, and Reliable operation, pointing to production deployment inside a vertical workflow. **6 / 6** Real-system integration**6 / 6** Reliable operation Databricks ### Platform operation first Reliable operation covers 8 of 8 JDs, and Safety / permissions covers 6 of 8, tying Agent demand closely to the operating boundary of a data platform. **8 / 8** Reliable operation**6 / 8** Safety / permissions 03 · Ten-group demand map ## Applications drive the most volume; company-specific bottlenecks create the real differentiation The ten job groups form the Agent demand map. Application Engineering has 77 JDs, Research / Model Behavior 50, and Runtime / Platform 34, together accounting for 62.2%; company-level priorities, however, do not simply follow the market total. ### Visible hiring demand across ten job groups Application Engineering is the largest demand group, but the most postings do not necessarily imply the greatest scarcity. A later section aligns the same job-group framework with 513 observed professionals. Application Engineering 77 JD · 18 companies Research / Model Behavior 50 JD · 11 companies Runtime / Platform 34 JD · 11 companies Deployment Engineering 19 JD · 5 companies Product / Design 16 JD · 9 companies Post-training / Robustness 14 JD · 4 companies Evals / Quality 13 JD · 5 companies Orchestration / Workflow 13 JD · 2 companies Solutions Architecture 12 JD · 5 companies Safety / Governance 11 JD · 5 companies Data source: Metix AI ### Ten-group hiring mix across four focus companies The complete JD base reveals four hiring recipes. OpenAI's 33 roles cover 8 of the 10 job groups, led by Application Engineering (10) while also investing in Post-training / Robustness (6) and Safety / Governance (5). Anthropic has 6 Research / Model Behavior roles among 16. Scale AI has 11 Application Engineering and 9 Research / Model Behavior roles among 35. Google assigns 11 of 22 roles to Deployment Engineering. #### OpenAI **33** JD - Application Engineering **10** - Research / Model Behavior **4** - Runtime / Platform **3** - Post-training / Robustness **6** - Safety / Governance **5** #### Anthropic **16** JD - Application Engineering **3** - Research / Model Behavior **6** - Runtime / Platform **3** - Post-training / Robustness **1** - Evals / Quality **3** #### Scale AI **35** JD - Application Engineering **11** - Research / Model Behavior **9** - Product / Design **3** - Post-training / Robustness **6** - Evals / Quality **6** #### Google **22** JD - Application Engineering **4** - Runtime / Platform **3** - Deployment Engineering **11** - Product / Design **2** - Safety / Governance **2** Data source: Metix AI 04 · JD competition groups ## 259 JDs form ten competition lanes The ten competition lanes correspond to ten primary deliverables. Companies compete for candidates within the same work object; roles carrying the Agent label but tied to different work objects have different competition boundaries. Capability terms explain bottleneck differences without changing the competition boundary. 01 ### Application Engineering Builds user-facing or domain-specific Agent products, features, and applications. **77** JDs**18** companies Scale AI **11**OpenAI **10**Salesforce **9**ServiceNow **7**Harvey **6**Databricks **4**Decagon **4**Google **4**Microsoft **4**Anthropic **3**Perplexity **3**Snowflake **3**Abridge **2**Hippocratic AI **2**Writer **2**Adobe **1**Cognition **1**LangChain **1** View representative roles - Senior / Staff Software Engineer, Developer Experience - Software Engineer - Early Career - Senior Engineering Manager – Agentic Product - Senior Staff Software Engineer, API 02 ### Research / Model Behavior Researches Agent capabilities, model behavior, retrieval, planning, and applied model systems. **50** JDs**11** companies ServiceNow **10**Scale AI **9**Meta **7**Anthropic **6**Microsoft **6**OpenAI **4**Adobe **2**Salesforce **2**Snowflake **2**Databricks **1**Hebbia **1** View representative roles - Senior Machine Learning Engineer - Staff Agentic ML Engineer - Photoshop - Model Performance Software Engineer, Claude Code - Research Engineer, Computer Use 03 ### Runtime / Platform Builds runtimes, harnesses, state, tool use, observability, and platform infrastructure. **34** JDs**11** companies ServiceNow **9**Adobe **6**Anthropic **3**Google **3**OpenAI **3**CrewAI **2**Glean **2**Hebbia **2**Salesforce **2**Databricks **1**Replit **1** View representative roles - Senior Software Engineer, Meta Factory Agent Harness - Software Engineer — Model & Partnerships Management (Agentic Builders Experience team) - Chief Architect for Adobe Intelligence Platform - Senior AI Platform Engineer- Data and Systems 04 ### Deployment Engineering Integrates, launches, operates, and troubleshoots Agents in customer environments. **19** JDs**5** companies Google **11**Decagon **3**Hippocratic AI **2**OpenAI **2**Replit **1** View representative roles - Customer Engineer, Agent Builder - Director of Customer Engineering, Agent Builder - Forward Deployed Engineer III, Generative AI, Google Cloud - Forward Deployed Engineer IV, Applied AI, Google Cloud 05 ### Product / Design Owns Agent product definition, interaction experience, design, and product direction. **16** JDs**9** companies Microsoft **3**Scale AI **3**Decagon **2**Google **2**Salesforce **2**Adobe **1**CrewAI **1**EvenUp **1**OpenAI **1** View representative roles - Principal Product Manager - Product Manager, Agent Experience - Senior Agent Product Manager - Senior/Staff/Principal Product Manager - AI Orchestration & Agentic Workflows 06 ### Post-training / Robustness Improves Agent behavior through post-training, reinforcement learning, rewards, and robustness. **14** JDs**4** companies OpenAI **6**Scale AI **6**Anthropic **1**Snowflake **1** View representative roles - Research Engineer, Machine Learning (Reinforcement Learning) - Agent Post-Training, Frontier Evals and Environments Research - Agent Post-Training Research - Agent Post-Training, API & Power Users 07 ### Evals / Quality Builds Agent evaluations, benchmarks, quality measurement, and feedback loops. **13** JDs**5** companies Scale AI **6**Anthropic **3**OpenAI **2**Databricks **1**ServiceNow **1** View representative roles - Data Operations Manager, Human Data - Engineering Manager, Agent Prompts & Evals - Staff Software Engineer - Agent Quality - Backend Software Engineer (Evals) 08 ### Orchestration / Workflow Builds multi-step workflows, planning, routing, state, and multi-Agent coordination. **13** JDs**2** companies Salesforce **8**Decagon **5** View representative roles - Engineering Manager, Agent Orchestration - Senior Software Engineer, Agent Orchestration - Staff Software Engineer, Agent Orchestration - Senior/Lead AI Software Engineer, Agentforce for Supply Chain 09 ### Solutions Architecture Designs enterprise Agent solutions, integration architecture, cloud platforms, and delivery paths. **12** JDs**5** companies LangChain **5**Meta **3**Salesforce **2**Databricks **1**Microsoft **1** View representative roles - Specialist Solutions Architect - AI/ML - Solutions Architect (Austin) - Solutions Architect (Dallas) - Solutions Architect (NYC) 10 ### Safety / Governance Owns permissions, guardrails, secure execution, risk controls, and governance. **11** JDs**5** companies OpenAI **5**Google **2**Salesforce **2**Glean **1**Microsoft **1** View representative roles - Product Manager, Agent Security & Governance - Agentic Safety and Ecosystem Architect, Trust and Safety - Principal Product Manager, Agent 365 Security & Governance - Principal Software Engineer, Codex Cyber 05 · Shared foundation ## Frameworks are no longer the main story: Agent hiring pays for systems that connect, evaluate, and run reliably LangChain / LangGraph is the most-mentioned named framework, yet it appears in only 24 / 259 JDs. Reliable operation, Continuous evaluation, and Real-system integration appear in 226, 190, and 180 JDs—a 7.5–9.4× coverage gap. ### Production capabilities are far more common than framework keywords Frameworks remain implementation paths, but no longer represent the shared requirement for Agent talent. Operation, evaluation, and integration are the capabilities that consistently recur across companies. #### Cross-company production capabilities These are multi-label capability signals measured across all ten job groups Reliable operation 87.3% · 226 Continuous evaluation 73.4% · 190 Real-system integration 69.5% · 180 #### Named frameworks The highest reaches only 24 JDs LangChain / LangGraph 9.3% · 24 CrewAI 2.7% · 7 AutoGen 1.9% · 5 LlamaIndex 1.9% · 5 Data source: Metix AI 06 · Stock and scarcity ## Talent reservoirs and the most aggressive hirers are different company sets Microsoft and Salesforce together hold 273 / 513 observed Agent professionals, or 53.2%. Scale AI and OpenAI have 35 and 33 active JDs but only 8 and 10 observed current professionals. Within the same job-group framework, Research / Model Behavior is the clearest scarcity signal. ### Observed Agent talent by company Microsoft and Salesforce are the main reservoirs; high-demand OpenAI and Scale AI depend more on the external candidate market. Microsoft 139 · 27.1% Salesforce 134 · 26.1% Google 44 · 8.6% Meta 36 · 7.0% Decagon 33 · 6.4% ServiceNow 31 · 6.0% Adobe 15 · 2.9% Hippocratic AI 14 · 2.7% Sierra 11 · 2.1% OpenAI 10 · 1.9% Data source: Metix AI ### Active postings per 100 observed professionals Research / Model Behavior reaches 138.9 (50 postings / 36 professionals), the clearest scarcity signal. Safety / Governance reaches 157.1 (11 / 7) and Post-training / Robustness 140.0 (14 / 10), also showing earlier signs of pressure. Safety / Governance 157.1 · 11 / 7 Post-training / Robustness 140.0 · 14 / 10 Research / Model Behavior 138.9 · 50 / 36 Evals / Quality 100.0 · 13 / 13 Application Engineering 98.7 · 77 / 78 Runtime / Platform 94.4 · 34 / 36 Solutions Architecture 38.7 · 12 / 31 Deployment Engineering 27.5 · 19 / 69 Orchestration / Workflow 24.5 · 13 / 53 Product / Design 8.9 · 16 / 180 Data source: Metix AI City concentration San Francisco It holds 133 observed professionals (25.9%), while demand is more concentrated: San Francisco has 91 postings (35.1%) and the Bay Area totals 58.3%. New York also shows 15.1% of postings versus 5.3% of talent. Job-group stock 35.1% Product / Design is the largest observed stock (180 people), followed by Application Engineering (78) and Deployment Engineering (69). Public profile visibility differs by job group, so demand-stock ratios are more useful for prioritization. 07 · Sources and flows ## The sourcing map starts beyond AI labs An external prior employer is observable for 408 professionals. Amazon / AWS is the largest source with 21 people; Bain feeds 6 into Decagon, while Slack and MuleSoft together feed 8 into Salesforce. Cloud platforms, consulting delivery, and enterprise software ecosystems supply different capabilities. ### Leading prior employers before the current Agent team Amazon / AWS contributes 2.3 times the visible source volume of second-ranked Microsoft. Meta / Instagram, Google, LinkedIn, and Bain show why sourcing should extend beyond AI labs. Amazon / AWS 21 Microsoft 9 Meta / Instagram 8 LinkedIn 6 Bain & Company 6 Google 6 Slack 4 Cisco 4 MuleSoft 4 Data source: Metix AI ### Six clearest talent routes **Amazon / AWS → Microsoft** Cloud platform → enterprise AI platform **6** **Bain & Company → Decagon** Consulting delivery → customer-workflow Agent **6** **Amazon / AWS → Google** Cloud platform → orchestration and deployment **5** **Slack + MuleSoft → Salesforce** Enterprise software ecosystem → Agent platform **8** **Meta / Instagram → Google** Movement between large technology platforms **4** **Cisco + Deloitte + LinkedIn → Salesforce** Integration, delivery, and platform experience converges **9** Only the latest observable external-employer move is shown; professionals without sufficiently complete histories are excluded from routes. 01 ### Fix the JD competition group first First identify the primary deliverable within the unified ten-group taxonomy, then build the company list and compare companies and candidate pools inside that group. 02 ### Then expand evidence around the bottleneck Expand the search with evidence of operation, evaluation, integration, permissions, state, and domain delivery; a single framework name narrows the pool too early. 03 ### Choose the source ecosystem last Prioritize cloud platforms for runtime, consulting and delivery for customer workflows, and enterprise software ecosystems for enterprise Agent platforms. 08 · Notes ## This report focuses on publicly visible Agent job and talent evidence ### Coverage notes - The report uses 259 publicly visible Agent postings to observe company hiring priorities. - The report uses 513 observed current Agent professionals to study talent stock, sources, and movement. - Job groups: the same ten job groups connect hiring demand with talent stock, making company priorities comparable. - Flows: the latest prior external employer is observable for 408 people, covering 79.5% of the observed talent pool. ### Job-group definitions - The ten job groups cover Application Engineering, Research / Model Behavior, Runtime / Platform, Deployment Engineering, Product / Design, Post-training / Robustness, Evals / Quality, Orchestration / Workflow, Solutions Architecture, and Safety / Governance. - Job groups prioritize primary work object and responsibilities, with company brand, title keywords, and location as supporting evidence. - Capabilities, frameworks, and domain terms explain company bottlenecks; candidate competition remains bounded by the primary job group. ### Interpretation boundaries - 513 is a publicly visible lower bound, not the full Agent headcount at the covered companies; public profile completeness differs by job group. - Posting counts represent visible hiring demand, not budgets, hires, or net expansion. - Companies or job groups with fewer visible jobs or professionals are used to describe hiring focus, not to independently establish talent-competition intensity. - Demand-stock pressure compares job-group priorities within this report coverage and should not be read as a market-wide vacancy rate. ### Questions answered - How much current Agent talent is observed and where it concentrates by company, city, and job group. - Total demand across the ten job groups and each company's primary hiring focus. - Where hiring demand and current talent stock are mismatched by job group. - Which employers are the main talent sources and what movement routes are visible.