AI Agent talent structure Role mix, company gaps, sources, flows, and geography
All 259 strongly Agent-related JDs span ten job groups, yet the hiring recipes diverge sharply: OpenAI invests across Application Engineering, Post-training / Robustness, and Safety / Governance; half of Google's roles are Deployment Engineering; and Salesforce concentrates on Orchestration / Workflow.
Report date 2026-07-29Produced by Metix AICoverage U.S. · 32 companies
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
01The 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%.
02Application 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.
03Talent 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.
04The 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.
OpenAI33 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
Anthropic16 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 AI35 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
Google22 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
Salesforce27 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
ServiceNow27 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.
Only 17 of 259 market JDs mention Memory / state; LangChain reaches 5 of 6, while Agent orchestration reaches 6 of 6.
5 / 6Memory / state6 / 6Agent 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.
All 6 JDs saturate Real-system integration, Continuous evaluation, and Reliable operation, pointing to production deployment inside a vertical workflow.
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 / 8Reliable operation6 / 8Safety / 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
33JD
Application Engineering10
Research / Model Behavior4
Runtime / Platform3
Post-training / Robustness6
Safety / Governance5
Anthropic
16JD
Application Engineering3
Research / Model Behavior6
Runtime / Platform3
Post-training / Robustness1
Evals / Quality3
Scale AI
35JD
Application Engineering11
Research / Model Behavior9
Product / Design3
Post-training / Robustness6
Evals / Quality6
Google
22JD
Application Engineering4
Runtime / Platform3
Deployment Engineering11
Product / Design2
Safety / Governance2
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.
Owns permissions, guardrails, secure execution, risk controls, and governance.
11JDs5companies
OpenAI 5Google 2Salesforce 2Glean 1Microsoft 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 → MicrosoftCloud platform → enterprise AI platform
6
Bain & Company → DecagonConsulting delivery → customer-workflow Agent
6
Amazon / AWS → GoogleCloud platform → orchestration and deployment
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.
FAQ
Questions this report answers
What Agent supply-and-demand inputs does this structure report use?
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.
How many Agent JDs and current Agent professionals sit behind the ten job groups?
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.
How should this report be cited?
Metix AI Talent Intelligence, 2026-07-29. AI Agent talent structure | Metix AI. https://metix.ai/reports/mapping/ai-agent-talent-supply-demand-gap-2026
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