Build vs buy (agents)

Build vs buy is a strategic decision framework for evaluating whether to develop custom agent infrastructure in-house or adopt third-party solutions. This decision encompasses the complete lifecycle of agent systems, from initial development through ongoing maintenance, scaling, and evolution. The framework considers technical capabilities, organizational resources, time constraints, and long-term strategic alignment.

Why it matters

Cost considerations

The financial implications of build vs buy extend far beyond initial development costs. Building custom agent infrastructure requires sustained investment in engineering talent, infrastructure, and ongoing maintenance. Organizations typically underestimate total build costs by 200-300% when factoring in hidden expenses like security audits, compliance certifications, and technical debt remediation.

Third-party solutions shift costs from capital expenditure to operational expenditure, converting unpredictable development timelines into predictable subscription fees. However, vendor pricing models often scale non-linearly with usage, potentially creating cost explosions as agent deployments grow from pilot to production.

Time-to-market advantage

Speed to market represents a critical differentiator in agent deployment strategies. Third-party platforms enable organizations to launch production agents in weeks rather than months, capturing market opportunities before competitors. This velocity proves especially valuable in rapidly evolving domains where first-mover advantage creates defensible moats.

Building custom solutions delays time-to-market but provides opportunities for differentiation through unique capabilities. Organizations must weigh the opportunity cost of delayed launches against the potential competitive advantage of proprietary features.

Maintenance burden

Long-term maintenance represents one of the most underestimated aspects of custom agent development. Agent systems require continuous updates to accommodate new data sources, evolving user interfaces, changing security requirements, and emerging model capabilities. Internal teams must maintain expertise across multiple domains: machine learning operations, infrastructure management, security compliance, and application development.

Third-party solutions transfer maintenance burden to specialized vendors who distribute costs across multiple customers. These vendors typically provide automatic updates, security patches, and compatibility fixes as part of standard service agreements.

Concrete examples

Evaluation criteria framework

A systematic evaluation framework should assess capabilities across seven dimensions:

Functional requirements: Document specific agent behaviors needed for your use case. For computer-use agents, this includes supported applications, action primitives (click, type, scroll, drag), state management capabilities, and error recovery mechanisms. Map these requirements against vendor feature matrices and open-source project roadmaps.

Performance requirements: Define acceptable latency thresholds for agent actions. Computer-use agents typically require < 500ms for action planning and < 2s for complex multi-step operations. Evaluate vendors against these benchmarks using production-like workloads.

Integration requirements: Catalog existing systems that agents must interact with: authentication providers, monitoring platforms, data warehouses, and business applications. Third-party solutions excel when they provide pre-built connectors for your existing stack. Custom builds prove necessary when proprietary systems lack vendor support.

Compliance requirements: Document regulatory constraints including data residency, audit logging, access controls, and certification requirements (SOC 2, HIPAA, GDPR). Vendor solutions offering compliance certifications can reduce time-to-compliance by 6-12 months compared to building certified systems internally.

Customization requirements: Identify areas where unique capabilities drive competitive advantage. Custom agent routing logic, proprietary action primitives, or specialized observability requirements may justify custom development.

Scale requirements: Project expected agent volumes across different time horizons (3 months, 1 year, 3 years). Vendor solutions often provide elastic scaling with transparent pricing, while custom builds require capacity planning and infrastructure investment.

Team capabilities: Assess internal expertise in agent frameworks, reinforcement learning, computer vision (for screen understanding), and distributed systems. Gaps in critical capabilities significantly extend build timelines and increase risk.

TCO analysis

Total cost of ownership analysis should project costs across a 3-year horizon, accounting for both direct and indirect expenses:

Build scenario costs:

Initial development: 4-6 engineers × 6-12 months = $400K-$900K
Infrastructure: Development, staging, and production environments = $50K-$150K/year
Ongoing maintenance: 2-3 engineers = $300K-$500K/year
Security and compliance: Audits, penetration testing, certifications = $75K-$200K/year
Opportunity cost: Delayed market entry = $200K-$2M depending on market dynamics
3-year TCO: $1.5M-$4M

Buy scenario costs:

Platform subscription: $50K-$500K/year depending on usage tiers
Integration development: $75K-$200K one-time
Ongoing customization: $50K-$100K/year
Vendor management: 0.5 FTE = $75K/year
3-year TCO: $500K-$2M

This analysis assumes standard enterprise deployments. High-volume scenarios (>10M agent actions/month) may shift economics toward custom builds due to per-transaction vendor pricing.

Hybrid approaches

Sophisticated organizations often adopt hybrid strategies that balance speed and customization:

Core third-party, custom extensions: Use vendor platforms for foundational capabilities (action execution, state management, safety constraints) while building custom components for competitive differentiation. For example, leverage Anthropic's computer use API for core agent capabilities while developing proprietary planning algorithms or domain-specific action libraries.

Progressive build strategy: Start with third-party solutions to validate market fit and gather production requirements. Once usage patterns stabilize, selectively rebuild components where vendor limitations constrain growth or customization needs exceed vendor capabilities.

Multi-vendor composition: Combine specialized vendors for different capabilities: one platform for computer-use primitives, another for agentic UI components, and open-source tools for observability. This approach requires sophisticated integration capabilities but optimizes cost-performance ratios across the stack.

Common pitfalls

Underestimating build costs

Organizations consistently underestimate the full cost of custom agent development by focusing only on initial implementation while overlooking ongoing expenses:

Hidden complexity: Agent systems require expertise across multiple domains: natural language processing, computer vision for screen understanding, reinforcement learning for behavior optimization, distributed systems for scaling, and security engineering. Building complete teams with this breadth requires 8-12 specialized engineers, not the 2-3 typically budgeted.

Infrastructure overhead: Production agent systems require sophisticated infrastructure: distributed tracing for debugging agent behaviors, real-time monitoring for detecting anomalous actions, secure sandbox environments for safe execution, and data pipelines for continuous learning. Infrastructure costs often exceed application development costs.

Maintenance underestimation: Applications and websites that agents interact with change continuously. A single UI redesign can break dozens of agent workflows, requiring rapid response from engineering teams. Plan for 40-50% of engineering capacity devoted to maintenance and adaptation rather than new features.

Vendor lock-in risks

Dependency on third-party vendors creates strategic risks that require active management:

Proprietary primitives: Vendors often introduce proprietary abstractions that lack industry standards. Agent workflows built on vendor-specific action primitives, state management patterns, or observability integrations become difficult to migrate. Mitigate this by abstracting vendor APIs behind internal interfaces, enabling future vendor substitution.

Data portability: Agent execution traces, learned behaviors, and observability data represent valuable assets for continuous improvement. Ensure vendor contracts guarantee data export capabilities in standard formats (OpenTelemetry for traces, standard JSON for execution logs).

Pricing leverage: As agents become core to business operations, vendors gain pricing leverage. Organizations reporting >50% year-over-year price increases from agent platform vendors face difficult migration decisions. Establish clear contract terms including price increase caps and transition assistance provisions.

Premature optimization

Building custom solutions before validating product-market fit wastes resources:

Optimizing for scale before achieving fit: Organizations often build custom agent infrastructure to handle projected scale before validating that users actually want agent capabilities. This results in sophisticated systems solving non-existent problems.

Custom features without user validation: Building proprietary agent capabilities based on hypothesized differentiators rather than validated user needs creates features nobody uses. Start with vendor solutions to validate which capabilities actually drive adoption.

Technical perfectionism: Engineering teams frequently overbuild initial agent systems, implementing sophisticated error recovery, multi-region redundancy, and advanced observability before achieving basic functionality. This delays learning and wastes resources on capabilities that may prove unnecessary.

Implementation

Decision framework

Apply this decision tree systematically:

Step 1: Assess strategic differentiation

Will custom agent capabilities create defensible competitive advantage?
Do proprietary workflows, data, or business logic require custom agent behaviors?
If NO to both: Strong bias toward third-party solutions

Step 2: Evaluate team capabilities

Does your team have production experience with agent frameworks, LLM operations, and computer-use systems?
Can you attract and retain specialized talent in agent development?
If NO to either: Strong bias toward third-party solutions

Step 3: Analyze time constraints

Is rapid market entry critical to capturing opportunity?
Do you need production agents within 3 months?
If YES to either: Strong bias toward third-party solutions

Step 4: Calculate economic breakeven

Project 3-year TCO for build vs buy scenarios
If buy scenario < 60% of build scenario: Choose third-party
If build scenario < buy scenario AND strategic differentiation is high: Consider custom build
Otherwise: Choose third-party with progressive build strategy

Step 5: Assess vendor maturity

Are there multiple viable vendors in the space?
Do vendors demonstrate stable pricing and reliable operations?
If NO to either: Consider build or wait for market maturation

Integration strategies

Successful integration of third-party agent platforms requires deliberate architecture:

Abstraction layer design: Implement internal interfaces that abstract vendor-specific APIs. This enables future vendor substitution without rewriting application code. For example, define internal primitives like execute_action(action_type, parameters) that map to vendor-specific implementations.

Observability integration: Connect vendor platforms to your existing observability stack. Third-party agents should emit traces to your distributed tracing system (Jaeger, Honeycomb), metrics to your monitoring platform (Datadog, Prometheus), and logs to your centralized logging (Elasticsearch, Splunk). Many vendors support OpenTelemetry for seamless integration.

Security boundary enforcement: Third-party agents execute within your infrastructure or access your systems. Implement defense-in-depth: network segmentation to limit agent access scope, credential management using short-lived tokens, and action approval workflows for high-risk operations.

Gradual rollout: Deploy third-party agents progressively: start with read-only operations, expand to low-risk write operations, and finally enable high-impact actions. Monitor error rates, user satisfaction, and business metrics at each stage before expanding scope.

Migration paths

Organizations frequently need to migrate between solutions as requirements evolve:

Buy-to-build migration: As agent usage scales or customization needs grow, organizations may migrate from vendor solutions to custom builds. Execute this as progressive replacement: identify highest-value components for custom development, build parallel implementations, validate parity through A/B testing, and gradually shift traffic. Maintain vendor integration as fallback during transition.

Build-to-buy migration: Organizations that built custom solutions early may migrate to mature vendor platforms to reduce maintenance burden. This migration proves more challenging due to proprietary primitives and data formats. Execute through API compatibility layer: implement vendor APIs as facade over existing custom system, gradually migrate components to vendor platform, and eventually deprecate custom implementations.

Vendor switching: Migrating between vendors requires abstraction layers established upfront. Organizations lacking abstraction face costly rewrites of agent workflows. If facing vendor migration, prioritize stateless agent patterns that minimize vendor-specific state dependencies.

Key metrics to track

Development velocity

Track agent development productivity across build vs buy scenarios:

Time to first agent: Measure duration from project initiation to first production agent serving real users. Third-party platforms typically achieve this in 2-4 weeks, while custom builds require 3-6 months.

Time to feature: Track duration from feature ideation to production deployment. Vendor platforms excel at standard capabilities but delay proprietary features pending roadmap prioritization. Custom builds enable rapid deployment of unique features but slower delivery of commodity capabilities.

Team utilization: Measure percentage of engineering time spent on differentiated capabilities vs undifferentiated infrastructure. Teams building custom agent platforms often spend < 30% of time on business logic, with the majority devoted to infrastructure concerns.

Total cost of ownership

Monitor comprehensive cost metrics across the agent lifecycle:

Per-agent cost: Calculate fully-loaded cost per agent interaction including platform fees, infrastructure, engineering allocation, and overhead. Track trends over time as usage scales to identify non-linear cost growth.

Engineering cost allocation: Measure engineering hours spent on agent development, maintenance, operations, and support. Custom builds typically require 2-3x engineering hours compared to vendor solutions for equivalent functionality.

Opportunity cost: Estimate revenue impact of delayed launches or features foregone due to resource constraints. This often represents the largest component of TCO but receives insufficient attention in decision-making.

Customization needs

Quantify customization requirements to validate build decisions:

Proprietary capability ratio: Calculate percentage of agent capabilities that provide competitive differentiation vs commodity features. If < 30% of capabilities are truly proprietary, custom builds rarely justify their cost.

Vendor gap analysis: For third-party solutions, track features requiring custom development to supplement vendor capabilities. If custom development exceeds 40% of total effort, evaluate building comprehensive custom solutions.

Configuration vs code ratio: Measure extent to which agent behaviors can be modified through configuration vs code changes. Higher configuration ratios indicate more maintainable systems regardless of build vs buy choice.

Related concepts

Computer-use agent: Autonomous systems that interact with software interfaces designed for humans
Agentic UI: User interface patterns optimized for agent interaction and human oversight
Observability: Systems for monitoring, debugging, and understanding agent behavior in production
Instrumentation: Technical implementation of logging, tracing, and metrics collection for agent systems