The enterprise AI agent market grew from $7.9 billion in 2025 to a projected $236 billion by 2034 at 45.8% CAGR. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by end of 2026, up from less than 5% in 2025. Every major enterprise platform — Salesforce, Microsoft, ServiceNow, SAP, UiPath — now has an agent offering. The shift from "AI experiments" to "production agents" happened faster than predicted. But the landscape is fragmented: SaaS platforms (configure-and-deploy), cloud-native services (build-on-infrastructure), and open-source frameworks (code-from-scratch) serve fundamentally different needs. Choosing wrong costs 6–12 months.

SaaS agent platforms embed AI directly into the enterprise software you already run. Salesforce Agentforce leads with 8,000+ customers and $900M in AI revenue within 6 months of launch. It excels at CRM-centric workflows: sales coaching, service handoffs, and marketing automation, with pre-built agents and an Agent Exchange marketplace. Pricing is action-based at $0.10 per action. Microsoft Copilot Studio has 230,000+ organizations using it, with strength in Microsoft 365/Teams/SharePoint ecosystems. ServiceNow Now Assist targets $1B AI-specific revenue by 2026, with a new OpenAI partnership for voice agents, excelling at IT service management and HR operations. The SaaS model offers the fastest time-to-value but constrains you to the platform's boundaries.

Cloud-native agent services offer more flexibility than SaaS platforms at the cost of more engineering effort. AWS Bedrock Agents provides access to 7+ model providers (Claude, Llama, Mistral, Cohere) with seamless IAM, VPC, and Lambda integration — rated 10/10 for security with SOC 2, HIPAA, and GDPR compliance. Google Vertex AI Agent Builder excels at multimodal workloads via Gemini with native Google Search grounding and tight BigQuery integration. Azure AI Studio offers the cheapest low-volume pricing and strong Copilot integration. The cloud approach is best when you need model flexibility, custom tool integration, or cross-platform agents that span multiple enterprise systems.

Open-source agent frameworks offer maximum control and the lowest per-agent cost — 55% lower than pure platform solutions — but require 2.3x more initial setup time and strong engineering teams. LangGraph (by LangChain, 25k GitHub stars) provides granular control over cyclic workflows with token-level streaming and conditional edge routing — best for complex stateful agents but with a steep learning curve. CrewAI (44.6k stars) offers role-based multi-agent collaboration with faster idea-to-production cycles — best for content generation, research, and parallel agent workflows. Pydantic AI (15.1k stars) is the type-safe newcomer gaining traction for its developer experience. Forrester predicts 75% of companies attempting to build their own agentic systems will fail.

Build when AI is your competitive advantage — when it's your product or proprietary IP. Buy for everything else. Purchased vendor solutions have a 67% success rate compared to 33% for internal builds. The reasons are structural: the field evolves too rapidly for most teams to keep up, there's a wide gap between working prototypes and production-grade deployments, data preparation accounts for up to 80% of total project effort, and infrastructure shifts faster than organizations can standardize. Even Lambda — a $4B+ AI company with world-class engineers — chose to buy rather than build. Only 11% of organizations have AI agents in production, and Forrester predicts 75% of companies attempting to build their own agentic systems will fail.

Beyond the technology choice, the engagement model determines outcomes. Consulting firms (Deloitte, Accenture, McKinsey) charge $500K–$2M+ for a first production agent over 6–12+ month engagements — their incentive structure rewards longer engagements rather than speed. SaaS platforms (Salesforce, ServiceNow, Glean) offer per-user or usage pricing with days-to-weeks deployment — predictable costs but limited scope. The emerging third model is agent platform + embedded engineering: per-agent pricing with Forward Deployed Engineers included, typically a 3-month POC with measurable outcomes, after which business teams own operations. Each model has its place, but the choice should match your organization's maturity and internal capability.

Before evaluating vendors, assess your own readiness across five pillars. Business problem: do you have a specific, measurable use case identified? Data foundations: is the data for that use case clean and accessible? Infrastructure: do you have cloud or on-prem capacity for AI workloads? Organizational culture: do you have leadership buy-in and team adaptability? Governance baseline: are data privacy and access controls in place? Vendors can't compensate for gaps in these pillars — they'll just be more expensive failures. Once ready, evaluate vendors on model-agnosticism (avoid lock-in to a single LLM provider), human-in-the-loop controls, audit trails, and total cost of ownership including ongoing maintenance.

The AI landscape changes so fast that today's best vendor may be tomorrow's legacy system. Vendor lock-in in AI agents manifests in three ways: model lock-in (tied to a single LLM provider whose pricing or quality may change), platform lock-in (agent logic embedded in proprietary tools that can't be exported), and data lock-in (training data, fine-tuning, and feedback loops trapped in a vendor's system). The mitigation strategy is an abstraction layer: use model-agnostic platforms that support multiple LLM providers, keep agent logic in portable formats (code, not drag-and-drop), and ensure all data — prompts, feedback, evaluations — can be exported. Evaluate platforms that support Anthropic, OpenAI, and Google models simultaneously.