Build Your Own AI Agent with Agent SDKs (2026)

🤔 Curiosity: Is the real question now “Which agent harness?”

The agent ecosystem is moving fast. In production, I’ve learned the model matters — but the harness matters more: planning, tool execution, memory, long‑running workflows, safety gates. Today we don’t have to build all of that from scratch because Agent SDKs are turning agent runtimes into embeddable infrastructure.

So the question shifts from “Which model?” to “Which harness scales my product?”

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📚 Retrieve: A quick map of the major Agent SDKs

Below is a compact view of the most visible SDKs right now, and what they emphasize.

✅ SDK Landscape (2026)

SDK	Strengths	Langs	Notes
OpenAI Agents SDK	Multi‑agent workflows, guardrails, tracing, sessions	Py/TS	Provider‑agnostic; strong tooling + tracing story
Google ADK	Code‑first orchestration, strong tool ecosystem, deploy to Vertex	Py (+ Java/Go/TS)	Model‑agnostic; rich agent tooling + evaluation
Claude Agent SDK	Production runtime with Claude Code, built‑in tools + hooks	Py/TS	CLI bundled; in‑process MCP tools
GitHub Copilot SDK	Copilot CLI runtime embedded via SDK	Py/TS/Go/.NET	Technical preview; CLI server mode
Strands Agents	AWS‑native, model‑agnostic, MCP built‑in	Py/TS	Strong multi‑provider support
Kimi Agent SDK	Thin SDK over Kimi CLI	Py/TS/Go	Reuses CLI tools + MCP servers
Codex SDK	TS SDK inside Codex repo	TS	Early stage; ties into Codex tooling
Gemini CLI SDK	TS SDK inside Gemini CLI	TS	Early stage; CLI‑first workflows

📌 SDK Cards (Images)

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🧩 What these SDKs actually solve

When I map these SDKs onto real production needs, they cluster around four jobs:

1. Orchestration (multi‑agent routing, handoffs, coordinator patterns)
2. Tooling (safe tool execution, approvals, MCP, custom tools)
3. Memory (sessions, persistent context, replayable traces)
4. Observability (tracing, guardrails, review loops)

If you’re shipping real features, the harness is your leverage. The SDK choice defines how quickly you can:

• move from prototype → production
• audit and debug failures
• scale multi‑agent workflows safely

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🛠️ Example: a minimal “harness‑first” pattern

from agents import Agent, Runner

planner = Agent(
    name="Planner",
    instructions="Break tasks into steps and assign tools",
)

executor = Agent(
    name="Executor",
    instructions="Run tools safely and report results",
)

# Simple handoff‑style pattern
triage = Agent(
    name="Triage",
    instructions="Decide if planning or execution is needed",
    handoffs=[planner, executor],
)

result = Runner.run_sync(triage, "Generate a build plan for a game AI pipeline")
print(result.final_output)

The point isn’t the syntax. It’s the structure: planning → execution → validation. That’s the harness, not the model.

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💡 Innovation: How I’d choose a harness in production

Here’s the practical decision tree I use:

1) If you need a rich tool ecosystem + deployment (Google stack) → ADK 2) If you need tracing + guardrails + provider‑agnostic agents → OpenAI Agents SDK 3) If you want CLI‑grade file/tool control out‑of‑box → Claude Agent SDK or Copilot SDK 4) If you want AWS‑native control + MCP first → Strands Agents 5) If your team already uses Kimi/Gemini/Codex CLI → pick their SDK for lowest friction

Key Takeaways

Insight	Implication	Next Step
Harness choice shapes quality + speed	Models are only half the story	Invest in orchestration + tooling
SDKs reduce infra overhead	Faster to production	Prototype with 2–3 SDKs
Observability matters	Debugging is the bottleneck	Pick SDKs with tracing/hooks

New questions I’m asking

• What’s the minimum harness for safe long‑running agents?
• Will SDKs converge around MCP + tracing + eval as defaults?
• Can we benchmark harness quality the way we benchmark models?

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References

• OpenAI Agents SDK: https://github.com/openai/openai-agents-python
• Google ADK: https://github.com/google/adk-python
• Claude Agent SDK: https://github.com/anthropics/claude-agent-sdk-python
• GitHub Copilot SDK: https://github.com/github/copilot-sdk
• Strands Agents SDK: https://github.com/strands-agents/sdk-python
• Kimi Agent SDK: https://github.com/MoonshotAI/kimi-agent-sdk
• Codex SDK (TS): https://github.com/openai/codex/tree/main/sdk/typescript
• Gemini CLI SDK (TS): https://github.com/google-gemini/gemini-cli/tree/main/packages/sdk