Playbook
Claude Agent SDK vs. OpenAI Agents SDK vs. Google ADK — An Enterprise Architecture Decision Framework
$201.9B
Agentic AI spending in 2026
Gartner, 2026
The SDK you pick today locks your portfolio into an ecosystem for years. This isn’t a benchmark comparison. It’s an architecture decision.
01 — Stakes
In the span of eight days, the entire agentic infrastructure layer reshuffled.
April 8
Anthropic launched Claude Managed Agents
Sandboxed execution, session persistence, multi-agent coordination at $0.08/session-hour.
April 15
OpenAI shipped the biggest Agents SDK update
Model-native harness with sandbox execution, workspace manifests, and 7 built-in sandbox providers.
Ongoing
Google ADK added TypeScript, deeper Vertex AI integration
Enterprise connectors for Salesforce, Workday, and SAP.
Models are increasingly interchangeable. Frameworks are not. A model swap is a config change. A framework swap is a rewrite. The agent SDK you adopt determines your orchestration patterns, your tool integration layer, your observability stack, your security model, and your cloud vendor gravity.
0%
of enterprise apps will embed task-specific AI agents by year-end
Gartner, 2026
0%
growth in agentic AI spending this year
Gartner, 2026
02 — The Three Paradigms
"Give the agent a computer."
Direct OS access: Bash, file system, web browsing as built-in primitives
8 tools out of the box: Read, Write, Edit, Bash, Glob, Grep, WebSearch, WebFetch
MCP (Model Context Protocol) — open standard under Linux Foundation
10,000+ active MCP servers, 97M monthly SDK downloads
Multi-agent via subagents with lifecycle hooks
Managed Agents: sandboxed execution at $0.08/session-hour
Multi-cloud: AWS Bedrock, Google Vertex AI, Azure AI Foundry + direct API
Where it leads
32% of enterprise LLM workloads. Reasoning quality. Deep OS access. Open-protocol extensibility.
"Batteries included, developer-first."
Most opinionated SDK — handoffs, guardrails, built-in tracing
Native sandbox execution with model-native harness (April 15)
Manifest abstraction for portable workspace descriptions
7 sandbox providers: Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel
Security-separated harness and compute layers
State externalization with snapshotting and rehydration
Built-in voice via Realtime API — TTS, interruption detection, SIP
Where it leads
Developer experience. Voice and multimodal. Polished tooling. Fastest time to first demo.
"Enterprise infrastructure, code-first."
Four first-class languages: Python, TypeScript, Java, Go
Graph-based workflows: sequential, parallel, loop execution
Deploy to Vertex AI Agent Engine, Cloud Run, or GKE
Inherited auth, Cloud Trace observability, enterprise security
Cloud API Registry for tool governance
Pre-built connectors: Salesforce, Workday, SAP, Spanner, AlloyDB
Agent-to-Agent (A2A) protocol for isolated memory
Where it leads
Enterprise governance. GCP-native deployment. Multi-language support. Widest connector ecosystem.
03 — Decision Matrix
Four decision factors. Each one narrows your choice. The visual centerpiece for CTO-level readers.
Decision Factor 1
Where does your infrastructure live?
| If your stack is… | Choose | Why |
|---|---|---|
| AWS-primary | Claude Agent SDK | Native Bedrock integration; multi-cloud flexibility preserves optionality |
| GCP-primary | Google ADK | Agent Engine, Cloud Run, GKE deployment with inherited security and auth |
| Azure-primary | Claude Agent SDK | Claude via Azure AI Foundry; OpenAI via native Azure OpenAI Service |
| Multi-cloud | Claude Agent SDK | Only SDK that deploys natively across all three hyperscalers plus direct API |
Decision Factor 2
What does your agent need to do?
| If your stack is… | Choose | Why |
|---|---|---|
| Execute code, edit files, run shell commands | Claude Agent SDK | Only SDK with native OS access as a built-in primitive |
| Handle voice interactions and multimodal input | OpenAI Agents SDK | Only SDK with built-in Realtime API, TTS, and SIP support |
| Orchestrate complex branching workflows | Google ADK | Graph-based execution with native parallel, sequential, and loop patterns |
| Connect to enterprise SaaS (Salesforce, Workday, SAP) | Google ADK | Pre-built enterprise connectors out of the box |
| Integrate with a wide ecosystem of community tools | Claude Agent SDK | 10,000+ MCP servers, 97M monthly SDK downloads |
Decision Factor 3
What's your regulatory environment?
| If your stack is… | Choose | Why |
|---|---|---|
| Heavily regulated (healthcare, financial services) | Claude Agent SDK | PwC partnership for compliance-critical deployments, multi-cloud auditability |
| EU AI Act high-risk categories | Google ADK | Tightest integration with enterprise audit trails, identity management, governance |
| Standard enterprise compliance | Claude Agent SDK | All three support adequate security and tracing for standard requirements |
Decision Factor 4
What's your team's composition?
| If your stack is… | Choose | Why |
|---|---|---|
| Python-heavy | Claude Agent SDK | All three have strong Python support — choose based on other factors |
| TypeScript-heavy | Claude Agent SDK | Both Claude and OpenAI have first-class TypeScript |
| Java or Go shops | Google ADK | Only SDK with Java and Go support |
| Small team, fast shipping | OpenAI Agents SDK | Most opinionated, fastest to first working agent, best built-in tracing |
04 — Lock-In Calculus
The question isn't whether you're locked in — it's how deeply, and what your exit costs.
Framework Lock-in
Model Lock-in
MCP is an open standard under the Linux Foundation. Your tool integrations are portable. Orchestration patterns (subagents, hooks) are Claude-specific but architecturally simple. The real lock-in is model quality — 32% enterprise share for a reason.
Exit Cost: Medium
Tool layer portable via MCP. Orchestration logic requires rewrite. Model swap may degrade quality.
Framework Lock-in
Model Lock-in
Handoffs, guardrails, and the tracing system are SDK-specific patterns. Multi-provider sandbox support reduces infrastructure lock-in. But voice/multimodal workflows are deeply integrated with the Realtime API.
Exit Cost: Medium-High
Orchestration patterns require rewrite. Voice/multimodal workflows have no portable equivalent. Sandbox layer is portable via Manifest.
Framework Lock-in
Model Lock-in
ADK is model-agnostic — it runs Claude, GPT, and Gemini. The lock-in is infrastructure: Agent Engine, Cloud Run, GKE, Cloud API Registry, enterprise connectors, Cloud Trace. Every production feature pulls you deeper into GCP.
Exit Cost: High
Infrastructure tightly coupled to GCP. Model layer is portable. Enterprise connector rewrites are expensive.
After Builder.ai’s collapse, one manufacturer spent $315K and three months migrating 40 AI workflows off a single-vendor platform. No vendor relationship should begin without an exit plan.
CloudPro Enterprise AI Risk Assessment, 2026
05 — Head-to-Head
| Capability | Claude | OpenAI | |
|---|---|---|---|
| Reasoning quality | |||
| Tool use accuracy (MCP-Atlas)77.3% vs 68.1% vs 73.9% | |||
| Developer experience | |||
| Voice / multimodal | |||
| Enterprise governance | |||
| Multi-cloud flexibility | |||
| Language support | |||
| Tool ecosystem | |||
| Cost controls | |||
| Sandbox execution | |||
| State management | |||
| Open standards |
MCP-Atlas tests end-to-end success across 36 real MCP servers and 220 tools using prompts that never name the specific tool required. For agentic workflows where tool selection is autonomous — which is most production scenarios — the gap between Claude (77.3%) and OpenAI (68.1%) compounds across every step of the chain.
06 — Deployment Archetypes
A PE ops team building agents for portfolio monitoring, board reporting, and financial analysis across 15 portfolio companies. Each portco has different tech stacks. Regulatory requirements vary.
Multi-cloud deployment — no infra changes at portco level
MCP's open protocol connects to whatever tools the portco uses
32% enterprise market share — battle-tested on these workflows
Cost controls via max_budget_usd prevent runaway spend
A healthcare portco building a patient intake and triage agent that handles phone calls, processes insurance information, and routes to the correct department — all via voice.
Native Realtime API — the only production-grade voice pipeline
Guardrails system handles compliance (PHI input, medical output)
Sandbox isolates insurance lookup code execution
Handoffs manage routing between specialized sub-agents
A SaaS portco running on Google Cloud, needing agents for Salesforce pipeline management, Workday HR operations, and internal tooling — with a team that includes Java and Go developers.
Agent Engine inherits GCP auth, observability, and security
Pre-built Salesforce and Workday connectors — zero custom integration
Java and Go support — full engineering team contributes
Graph-based orchestration for complex HR and sales branching logic
07 — Decision Flowchart
An interactive decision tree based on your actual deployment constraints.
Question 1 of 4
Does your agent need to handle voice or video?
08 — Our Advisory
Disclosure: We build on Claude Agent SDK. Our own marketing operation — the one that produced this playbook — runs on it. Take this with appropriate calibration.
The multi-cloud flexibility and MCP's open standard are non-negotiable when you're deploying across companies with heterogeneous stacks. The 32% enterprise market share isn't an accident — it's because the reasoning quality and tool accuracy at production scale are measurably ahead.
Nothing else has a native voice pipeline. The April sandbox update closed the biggest gap in their production story. The guardrails system is genuinely well-designed for compliance-sensitive customer interactions.
The inherited enterprise features from Agent Engine — auth, observability, security, connectors — save months of integration work. Four-language support means your full team ships, not just the Python developers.
If you can’t decide
Start with Claude Agent SDK. MCP is an open standard. Your tool integrations are portable. If you need to add voice later, you can integrate OpenAI’s Realtime API at the tool layer without rewriting your orchestration. If you move to GCP, Claude runs natively on Vertex AI.
The worst decision is the one that optimizes for today’s demo and ignores tomorrow’s exit.
$201.9B
agentic AI spending this year
40%
of enterprise apps embedding agents by December
90 days
until every portfolio company makes this choice
The model you pick is a quarterly decision.
The SDK you pick is a multi-year architectural commitment.
Choose the paradigm, not the benchmark.
Sources & References
OpenAI, "The Next Evolution of the Agents SDK" (April 15, 2026)
Help Net Security, "OpenAI Updates Agents SDK, Adds Sandbox" (April 16, 2026)
TechCrunch, "OpenAI Updates Its Agents SDK for Enterprise" (April 15, 2026)
SiliconANGLE, "Anthropic Launches Claude Managed Agents" (April 8, 2026)
Verdent, "Claude Managed Agents Pricing" (2026)
Composio, "Claude Agents SDK vs. OpenAI Agents SDK vs. Google ADK" (2026)
Google, "Agent Development Kit Documentation"
Kai Waehner, "Enterprise Agentic AI Landscape 2026" (April 6, 2026)
CData, "2026: The Year for Enterprise-Ready MCP Adoption"
MCP Blog, "MCP 2026 Roadmap"
GetPanto, "Claude AI Statistics 2026: Revenue, Users & Market Share"
Gartner, "40% of Enterprise Apps Will Feature AI Agents by 2026"
Gartner, "Worldwide AI Spending Will Total $2.5 Trillion in 2026"
PwC and Anthropic, "Enterprise AI Agent Deployment"
CloudPro, "AI Agent Vendor Lock-In: How to Manage Enterprise Risk" (2026)
Scale AI, "MCP-Atlas Benchmark"
More Posts
Ready to move
We design and deploy AI-native systems for companies moving fast in competitive markets.
Talk to LightCI