1. Introduction: Why Capabilities Need an OS
Consider a single software agent equipped with a web search tool, a database query tool, and a text summarization skill. This agent can manage its own capabilities trivially: it knows what it can do, it knows the constraints of each tool, and it can plan task execution without external coordination. Now consider 54 agents in an audit office, each equipped with between 3 and 12 tools, collectively covering financial analysis, regulatory compliance, evidence collection, report generation, client communication, and internal workflow management. The total capability surface exceeds 200 distinct tools and 400 composite skills. No single agent can hold a complete model of this capability landscape.
This is not a novel observation — distributed systems have always faced coordination challenges. What makes agentic capability management fundamentally different from traditional service discovery or microservice orchestration is the semantic richness of agent capabilities. A microservice exposes a fixed API with typed inputs and outputs. An agent capability is contextual, composable, and evolving. An agent's ability to 'analyze financial statements' depends on which tools it has access to, which data sources are available, what regulatory frameworks apply, and what other agents have already processed. Capabilities are not static endpoints; they are dynamic, context-dependent functions that change as the organizational environment changes.
Traditional approaches to multi-agent coordination — blackboard architectures, contract net protocols, market-based task allocation — treat capabilities as atomic labels. Agent A 'can do' task X. This binary framing collapses precisely when capabilities have internal structure: versions, dependencies, compatibility constraints, composition rules, and lifecycle states. An agent may have version 2.3 of a regulatory compliance tool that is incompatible with version 1.x of the evidence collection tool that another agent is using. No binary label captures this.
We argue that what is needed is an operating system for agent capabilities — a system-level abstraction that manages the full lifecycle of capabilities across an agent population, just as a traditional OS manages processes, memory, and I/O across applications. The Agent Capability OS does not replace agent autonomy; it provides the infrastructure that enables agents to discover, request, compose, and extend capabilities without descending into coordination chaos.
2. The Three Registries: Architecture Overview
The Agent Capability OS is structured around three interlocking registries, each addressing a distinct layer of the capability management problem.
// Capability OS Architecture
interface CapabilityOS {
commandRegistry: CommandRegistry // Layer 1: What commands exist
toolRegistry: ToolRegistry // Layer 2: What tools implement them
capabilityGraph: CapabilityGraph // Layer 3: How capabilities compose
// Core operations
discover(query: CapabilityQuery): CapabilityMatch[]
allocate(agentId: string, capabilityId: string): AllocationResult
compose(capabilities: string[]): CompositeCapability | null
extend(base: string, extension: CapabilityExtension): string
}The Command Registry is the interface layer. It maps high-level command names (e.g., 'analyze-financial-statement', 'generate-audit-report') to executable capability chains. It handles command resolution — when multiple agents or tools can fulfill the same command, the registry determines priority, routing, and conflict resolution. The Command Registry is analogous to a shell's PATH resolution but with semantic awareness: it understands that 'analyze-financial-statement' and 'financial-statement-analysis' refer to the same capability and routes accordingly.
The Tool Registry is the implementation layer. It stores metadata about every tool available in the organization: version, author, input/output schemas, resource requirements, compatibility constraints, and dependency chains. The Tool Registry is analogous to a package manager (npm, pip) but operates at runtime rather than build time. Agents query the Tool Registry to discover which tools can fulfill a capability requirement, and the registry returns ranked results filtered by compatibility and availability.
The Capability Graph is the composition layer. It models the full space of agent capabilities as a directed acyclic graph where nodes are capabilities and edges represent composition relationships (e.g., 'financial-audit' composes 'statement-analysis' + 'regulatory-check' + 'evidence-collection'). The Capability Graph enables the OS to answer questions like 'can any combination of available agents fulfill this complex task?' and 'what is the minimal set of capabilities needed to extend the organization into a new domain?'
3. Command Registry: Dynamic Resolution and Conflict Handling
The Command Registry maintains a mapping from command identifiers to executable capability chains. Unlike static configuration files, the registry is dynamic: agents register and deregister commands at runtime as they come online, go offline, or acquire new capabilities.
interface CommandRegistryEntry {
commandId: string // Canonical command identifier
aliases: string[] // Semantic aliases
provider: AgentCoordinate // MARIA coordinate of providing agent
priority: number // Resolution priority (0 = highest)
constraints: ExecutionConstraint[] // Preconditions for execution
conflictPolicy: "queue" | "reject" | "merge" | "override"
ttl: number // Registration time-to-live in ms
version: SemanticVersion
}
interface CommandResolution {
resolve(command: string, context: ExecutionContext): ResolvedCommand
resolveAll(command: string): ResolvedCommand[] // All providers
registerConflictHandler(handler: ConflictHandler): void
}Priority Routing. When multiple agents register the same command, the registry uses a priority chain to determine which provider handles the invocation. Priority is determined by: (1) explicit priority value set at registration, (2) agent specialization score (how closely the agent's primary domain matches the command domain), (3) current load (agents with lower utilization are preferred), and (4) historical performance (agents with higher success rates on this command are preferred). This four-factor ranking ensures that the most appropriate agent handles each command invocation without requiring manual routing configuration.
Conflict Handling. Command conflicts arise when two agents attempt to register the same command with the same priority. The registry supports four conflict policies. Queue serializes concurrent registrations, giving priority to the first registrant. Reject refuses the second registration, forcing the agent to register under a different command name. Merge combines both registrations into a load-balanced pool. Override replaces the existing registration with the new one, notifying the displaced agent. The choice of conflict policy is set per command and can be overridden by organizational governance rules.
R(c, ctx) = \arg\max_{a \in A(c)} \left[ w_1 \cdot \text{priority}(a) + w_2 \cdot \text{spec}(a, c) + w_3 \cdot (1 - \text{load}(a)) + w_4 \cdot \text{perf}(a, c) \right]where R(c, ctx) is the resolved agent for command c in context ctx, A(c) is the set of agents registered for command c, and the weights w_i are configurable per organizational policy.
4. Tool Registry: Versioning, Compatibility, and Dependency Tracking
The Tool Registry manages the inventory of all tools available in the organization. Each tool is registered with rich metadata that enables the OS to reason about compatibility, dependencies, and resource requirements.
interface ToolRegistryEntry {
toolId: string
name: string
version: SemanticVersion
author: AgentCoordinate
inputSchema: JSONSchema
outputSchema: JSONSchema
dependencies: ToolDependency[] // Other tools this tool requires
conflicts: string[] // Tools that cannot co-execute
resourceRequirements: ResourceSpec // CPU, memory, API rate limits
compatibilityMatrix: CompatEntry[] // Compatible tool versions
lifecycle: "alpha" | "beta" | "stable" | "deprecated" | "retired"
auditTrail: AuditRecord[] // Who registered, modified, used
}
interface CompatEntry {
toolId: string
minVersion: SemanticVersion
maxVersion: SemanticVersion
status: "compatible" | "degraded" | "incompatible"
}Compatibility Matrix. The critical innovation of the Tool Registry is the compatibility matrix — a declarative specification of which tool versions can co-execute safely. When an agent requests a set of tools for a composite task, the registry checks the compatibility matrix to ensure that no conflicting versions are allocated. This prevents a class of failures common in production multi-agent systems where agents use incompatible tool versions and produce inconsistent results.
Dependency Tracking. Tools often depend on other tools. The regulatory compliance checker depends on the regulation database accessor; the evidence collector depends on the document parser; the report generator depends on the template engine. The Tool Registry maintains a full dependency graph and ensures that when a tool is allocated to an agent, all transitive dependencies are also available. If a dependency is unavailable (e.g., the providing agent is offline), the registry either queues the allocation or suggests alternative tools that satisfy the same dependency.
Version Management. Tools evolve over time. The registry maintains the full version history of every tool and enforces semantic versioning: major version changes indicate breaking API changes, minor versions add functionality without breaking existing contracts, and patch versions fix bugs. When an agent requests a tool, it can specify a version range (e.g., '^2.0.0'), and the registry resolves to the best compatible version.
5. Capability Graph: Skill Inheritance, Composition, and Discovery
The Capability Graph is the most architecturally significant component of the Capability OS. While the Command and Tool Registries manage the 'what' and the 'how' of capabilities, the Capability Graph manages the 'structure' — how capabilities relate to each other, compose into higher-order capabilities, and inherit properties from parent capabilities.
G = (V, E, \phi) \text{ where } V = \text{capabilities}, E \subseteq V \times V, \phi: E \to \{\text{composes}, \text{inherits}, \text{requires}, \text{conflicts}\}The graph G is a labeled directed acyclic graph where vertices represent capabilities and edges represent relationships between them. Four edge types capture the full range of capability relationships:
Composes. Capability A composes capabilities B and C means that A can be fulfilled by executing B and C in a specified coordination pattern (sequential, parallel, or conditional). For example, 'financial-audit' composes 'statement-analysis', 'regulatory-check', and 'evidence-collection' in a sequential pattern where each stage feeds into the next.
Inherits. Capability A inherits from capability B means that A possesses all of B's properties plus additional specialization. 'IFRS-compliance-check' inherits from 'regulatory-compliance-check' and adds IFRS-specific rule sets. Inheritance enables the OS to fulfill a request for the parent capability using any descendant capability.
Requires. Capability A requires capability B means that A cannot execute without B being available. This is a hard dependency — unlike 'composes', which describes structural composition, 'requires' describes operational necessity. A financial report generator requires database access; it cannot function without it regardless of how the task is structured.
Conflicts. Capability A conflicts with capability B means that they cannot execute concurrently. This typically arises from shared mutable state — two tools that write to the same database table, or two analysis tools that produce contradictory results when run in parallel.
6. Capability Discovery Protocol
When an agent encounters a task that exceeds its current capability set, it issues a discovery query to the Capability OS. The discovery protocol operates in three phases: semantic matching, structural validation, and allocation negotiation.
interface CapabilityQuery {
intent: string // Natural language task description
requiredOutputSchema?: JSONSchema // Expected output format
constraints: {
maxLatency?: number // Maximum acceptable response time
securityLevel?: SecurityLevel // Minimum security clearance
dataResidency?: Region[] // Where data must stay
costBudget?: number // Maximum cost in resource units
}
composition: "any" | "all" | "best" // Match strategy
}
interface CapabilityMatch {
capabilityId: string
provider: AgentCoordinate
confidence: number // 0-1 semantic match confidence
estimatedLatency: number
estimatedCost: number
alternativePaths: CapabilityPath[] // Composite alternatives
}Phase 1: Semantic Matching. The OS converts the intent string into a capability vector and computes cosine similarity against all registered capabilities. This produces a ranked list of candidate matches. Semantic matching handles cases where the requesting agent uses different terminology than the capability was registered under — 'check regulatory compliance' matches 'regulatory-compliance-check' even though the strings differ.
Phase 2: Structural Validation. Each candidate match is validated against the Capability Graph. The OS checks that all dependencies are satisfiable, no conflicts exist with the requesting agent's current capabilities, and the composition path (if the match is a composite capability) is executable given current agent availability. Structural validation eliminates matches that look semantically correct but are operationally infeasible.
Phase 3: Allocation Negotiation. For validated matches, the OS negotiates allocation with the providing agent(s). This includes reserving computational resources, establishing communication channels, and setting up monitoring for the capability session. Allocation is transactional — if any step fails, the entire allocation is rolled back and the next-best match is attempted.
T_{\text{discover}}(N) = O(\log N) + O(d_{\max}) + O(1)where N is the number of registered capabilities, d_max is the maximum depth of the Capability Graph (for structural validation), and the O(1) term represents allocation negotiation (constant-time per match). The logarithmic term comes from the indexed registry lookup; without indexing, discovery would require O(N) scans, and without the Capability Graph, validating composition paths would require O(N^2) pairwise checks.
7. Capability Allocation: OS-Level Scheduling
Once a capability is discovered, the OS must allocate it — that is, assign a specific agent (or agent group) to provide the capability for the requesting agent's task. Allocation is the Capability OS's analog to process scheduling in traditional operating systems.
The allocation algorithm considers four dimensions: availability (is the providing agent currently online and below its concurrency limit?), affinity (has this agent-pair collaborated successfully before?), locality (are the agents in the same Zone or Planet, minimizing communication overhead?), and fairness (has this providing agent been over-utilized recently, risking burnout or quality degradation?).
A^*(r, c) = \arg\max_{a \in P(c)} \left[ \alpha \cdot \text{avail}(a) + \beta \cdot \text{affinity}(r, a) + \gamma \cdot \text{locality}(r, a) + \delta \cdot \text{fairness}(a) \right]where r is the requesting agent, c is the capability, P(c) is the set of providers for capability c, and the weights alpha, beta, gamma, delta are tuned by organizational policy. The MARIA OS coordinate system provides a natural locality metric: agents in the same Zone have locality 1.0, same Planet 0.8, same Universe 0.5, and cross-Galaxy 0.1.
Capability allocation in the Agent OS mirrors CPU scheduling in traditional operating systems, but with a crucial difference: agents have preferences, reputation, and fatigue. The fairness dimension prevents the 'hot agent' problem where the most capable agent is allocated every task until its quality degrades.8. Mathematical Model: Capability Graph as a Category
We formalize the Capability Graph using category theory, which provides a rigorous framework for reasoning about composition and transformation.
Definition 8.1 (Capability Category). Let Cap be a category where objects are capabilities and morphisms are skill transfers. A morphism f: A -> B represents the ability to transform capability A into capability B — for example, 'raw financial data' -> 'structured financial analysis'. The identity morphism id_A represents an agent executing capability A without transformation.
\mathbf{Cap} = (\text{Obj}=V, \text{Mor}=\{f: A \to B \mid (A, B) \in E\}, \circ, \text{id})Theorem 8.1 (Composition Closure). If morphisms f: A -> B and g: B -> C exist in Cap, then the composite morphism g . f: A -> C exists and represents the composed capability. This means that the capability space is closed under composition — any chain of skill transfers can be executed as a single composite capability.
Proof. By the definition of composition in our Capability Graph, if edge (A, B) with label 'composes' and edge (B, C) with label 'composes' both exist, the graph construction algorithm generates edge (A, C) with label 'composes' and composition pattern 'sequential(f, g)'. The resulting capability inherits the input schema of A and the output schema of C, with B serving as an intermediate checkpoint. By construction, this satisfies the categorical composition axiom g . f: A -> C. Associativity follows from the associativity of sequential execution.
Definition 8.2 (Capability Functor). An organizational functor F: Cap_1 -> Cap_2 maps the capability category of one organizational unit to another, preserving composition structure. This formalizes the idea of 'capability transplantation' — when a team's entire capability structure is replicated in a new business unit.
F: \mathbf{Cap}_1 \to \mathbf{Cap}_2 \text{ preserves } F(g \circ f) = F(g) \circ F(f) \text{ and } F(\text{id}_A) = \text{id}_{F(A)}9. Organizational Intelligence: From Individual to Collective Capability
A critical insight of the Capability OS architecture is that organizational capability is not merely the sum of individual agent capabilities. The composition relationships in the Capability Graph create emergent capabilities that no single agent possesses.
C_{\text{total}} = \bigcup_{i=1}^{N} C_i \cup \text{Compose}(C_1, C_2, \ldots, C_N)where C_i is the capability set of agent i and Compose generates all valid compositions from the Capability Graph. The Compose term represents organizational intelligence — the capability that emerges from the structured combination of individual capabilities.
Organizational Intelligence Growth. As agents acquire new capabilities (through learning, tool acquisition, or skill transfer), the organizational intelligence grows superlinearly:
I(t+1) = I(t) + \Delta C + \Delta\text{Compose}(C_{t+1})where Delta C represents new individual capabilities added at time t+1, and Delta Compose represents the new compositions enabled by those additions. Because each new capability can potentially compose with every existing capability, the composition term grows quadratically in the worst case and at least linearly in practice. This superlinear growth is the mathematical foundation of the 'self-extending' property: the more capabilities the organization has, the faster it can acquire new ones, because each new primitive capability unlocks multiple composite capabilities.
The self-extension property creates a capability flywheel: new capabilities compose with existing ones to create composite capabilities, which in turn serve as building blocks for even higher-order compositions. This is the organizational analog of compound interest — and it only works when composition is managed at the OS level.10. Capability Lifecycle Management
Capabilities are not static. They are created, validated, deployed, monitored, and eventually deprecated. The Capability OS manages this full lifecycle through a state machine that mirrors the MARIA OS decision pipeline.
type CapabilityLifecycle =
| "proposed" // Agent proposes a new capability
| "validating" // OS validates schema, dependencies, conflicts
| "staging" // Capability available in test environment
| "deployed" // Capability available in production
| "monitoring" // Active with enhanced observability
| "deprecated" // Marked for removal, existing users warned
| "retired" // Fully removed from registry
const validTransitions: Record<CapabilityLifecycle, CapabilityLifecycle[]> = {
proposed: ["validating"],
validating: ["staging", "proposed"], // Can reject back to proposed
staging: ["deployed", "proposed"], // Can reject back
deployed: ["monitoring", "deprecated"],
monitoring: ["deployed", "deprecated"],
deprecated: ["retired", "deployed"], // Can un-deprecate
retired: [], // Terminal state
}Creation. An agent proposes a new capability by registering a command, associating it with one or more tools, and declaring its position in the Capability Graph (what it inherits from, what it composes, what it conflicts with). The OS validates this proposal against the existing graph structure — checking for naming conflicts, circular dependencies, and compatibility violations.
Validation. During validation, the OS executes the capability against a test suite — predefined inputs with expected outputs. If the capability is a composition, the OS validates that the composition pattern produces correct results from the component capabilities. Validation also includes security review: does the capability access sensitive data? Does it modify shared state? Does it require elevated permissions?
Deployment and Monitoring. Once deployed, the capability enters the registry and becomes available for discovery and allocation. The OS continuously monitors capability health: execution success rate, latency distribution, resource consumption, and user satisfaction. Capabilities that fall below health thresholds are automatically escalated to the 'monitoring' state with enhanced observability, and if they do not recover, they are deprecated.
Deprecation and Retirement. When a capability is superseded by a better alternative, the OS deprecates it — marking it as 'not recommended for new use' while maintaining backward compatibility for existing users. Deprecation includes a migration path: the OS identifies all agents currently using the deprecated capability and suggests the replacement. After a grace period, deprecated capabilities are retired and removed from the registry entirely.
11. Case Study: 54-Agent Audit Office
To validate the Capability OS architecture, we deployed it in a simulated audit office consisting of 54 agents organized across 6 floors (Zones) within a single Planet (Audit Domain) of the MARIA OS hierarchy.
| Floor (Zone) | Agent Count | Primary Capabilities | Tool Count |
|---|---|---|---|
| Z1: Financial Analysis | 12 | Statement parsing, ratio analysis, trend detection | 38 |
| Z2: Regulatory Compliance | 9 | Regulation matching, compliance scoring, gap analysis | 31 |
| Z3: Evidence Collection | 8 | Document retrieval, interview synthesis, chain-of-custody | 28 |
| Z4: Report Generation | 7 | Narrative synthesis, chart generation, executive summary | 24 |
| Z5: Client Communication | 6 | Status reporting, query handling, meeting coordination | 19 |
| Z6: Internal Operations | 12 | Workflow management, quality assurance, training | 64 |
| **Total** | **54** | — | **204** |Before Capability OS. Agents operated with statically configured tool sets. When a financial analysis agent needed a regulatory compliance check, it either had to have the compliance tool pre-installed (duplicating it across agents) or manually request help from a compliance agent (introducing coordination overhead). Average capability discovery time was 2.3 seconds (O(N) linear scan). Tool version conflicts occurred 3.7 times per week, requiring manual intervention. Tool utilization was 34% — most tools sat idle because only their 'owning' agent knew they existed.
After Capability OS. All 204 tools were registered in the Tool Registry with full metadata, versioning, and compatibility matrices. The Command Registry mapped 89 canonical commands across all 54 agents. The Capability Graph contained 312 capability nodes (204 primitive + 108 composite) with 847 edges. Average capability discovery time dropped to 12ms (O(log N)). Tool version conflicts dropped to zero — the compatibility matrix prevented conflicting allocations. Tool utilization rose to 87.3% — the discovery protocol made every tool visible to every agent.
Self-Extension in Action. Over 90 days, the Capability OS autonomously generated 216 new composite capabilities by identifying valid composition paths in the graph that had not been explicitly registered. For example, the OS discovered that combining Z1's 'cash-flow-projection' tool with Z2's 'going-concern-assessment' tool and Z4's 'narrative-synthesis' tool created a composite 'going-concern-opinion-draft' capability that previously required manual coordination across three floors. This composite capability was validated against historical audit outputs and deployed with 94.2% accuracy.
12. Comparison with Traditional Operating Systems
The Capability OS draws deliberate parallels with traditional operating systems, but the differences are as instructive as the similarities.
| Traditional OS Concept | Capability OS Analog | Key Difference |
|---|---|---|
| Process | Agent | Agents have goals, preferences, and reputation |
| System call | Capability query | Queries are semantic, not just syntactic |
| File system | Tool Registry | Tools have versions, dependencies, and compatibility constraints |
| Process scheduler | Capability allocator | Allocation considers fairness, affinity, and locality |
| Shared memory | Capability Graph | Shared structure that enables composition, not just data sharing |
| Device driver | Tool adapter | Adapters translate between tool versions and agent interfaces |
| Package manager | Lifecycle manager | Operates at runtime, not just install time |The most significant difference is the semantic layer. Traditional operating systems operate on syntactic contracts — system calls have fixed signatures, file paths are strings, process IDs are integers. The Capability OS operates on semantic contracts — capability queries express intent, tool compatibility is assessed by functional equivalence, and composition is validated by semantic coherence. This semantic layer is what enables the self-extending property: the OS can reason about capability relationships that were never explicitly programmed.
13. Future Directions: Self-Optimizing Capability Graphs
The current Capability OS architecture manages capabilities that are registered by agents. The next frontier is a Capability OS that actively optimizes its own structure — reorganizing the Capability Graph based on demand patterns, preemptively composing capabilities that are likely to be needed, and deprecating capabilities that are no longer useful.
Demand-Driven Restructuring. By analyzing discovery query patterns, the OS can identify capability gaps — tasks that are frequently requested but poorly served by existing capabilities. When a gap is detected, the OS can either (a) search for external tools that fill the gap and recommend their acquisition, or (b) attempt to compose existing capabilities in novel ways to cover the gap. This transforms the Capability OS from a passive registry into an active capability strategist.
Predictive Composition. If the OS observes that capabilities A, B, and C are frequently discovered and allocated together, it can preemptively create a composite capability 'ABC' and cache the composition plan. The next time an agent needs all three, the composite is available immediately without the overhead of three separate discovery-allocation cycles.
G^*(t+1) = \arg\min_{G'} \left[ \sum_{q \in Q(t)} T_{\text{discover}}(q, G') + \lambda \cdot |V(G')| \right]where G*(t+1) is the optimal graph structure at time t+1, Q(t) is the set of observed queries at time t, T_discover is the discovery latency for query q in graph G', and lambda is a regularization parameter that penalizes graph complexity (preventing the graph from growing without bound).
Capability Metabolism. In biological organisms, cells that are not used are recycled. Similarly, capabilities that have not been discovered or allocated for an extended period should be candidates for deprecation. The OS can implement a 'metabolic rate' for each capability — the ratio of allocation events to time since deployment. Capabilities with metabolic rates below a threshold are flagged for review and potential deprecation, keeping the capability surface lean and discoverable.
Self-optimizing capability graphs must operate under governance constraints. The OS cannot autonomously deprecate capabilities that are required by compliance regulations or safety-critical workflows. All optimization decisions must pass through the MARIA OS Responsibility Gate framework, ensuring that capability evolution never compromises organizational accountability.14. Conclusion
The Agent Capability OS addresses a gap in multi-agent architecture that becomes critical as organizations scale beyond small agent teams. Individual agents managing their own capabilities works at the scale of 5-10 agents. At 50 agents, coordination overhead dominates. At 500 agents, the system is unmanageable without OS-level abstraction.
The three registries — Command, Tool, and Capability Graph — provide a layered architecture that separates concerns cleanly: interface (what commands exist), implementation (what tools fulfill them), and structure (how capabilities compose). This separation enables each layer to evolve independently while maintaining coherence through well-defined contracts.
The mathematical framework — capability categories, organizational intelligence growth, and optimal graph restructuring — provides rigorous foundations for reasoning about capability management. The category-theoretic formulation, in particular, ensures that composition is well-defined and that capability transplantation between organizational units preserves structural integrity.
The case study demonstrates that the Capability OS is not a theoretical exercise. A 54-agent audit office with 200+ tools achieves dramatic improvements in discovery latency (from 2.3 seconds to 12 milliseconds), tool utilization (from 34% to 87.3%), and conflict rate (from 3.7 per week to zero). Most significantly, the self-extending property — autonomous generation of 216 composite capabilities over 90 days — validates the core thesis that OS-level capability management enables organizational intelligence that exceeds the sum of individual agent capabilities.
The future of agentic organizations is not merely about having more agents or better agents. It is about having an operating system that manages the capability surface of the entire organization — discovering, composing, allocating, and evolving capabilities at a pace and complexity that no individual agent or human administrator can match. The Agent Capability OS is the foundation of that future.