Four pieces every public-data agent needs in 2026: a model, an MCP runtime, a unified data API, and observability. Here's a sample stack.

Building a production agent that reaches for the public web in 2026 needs four pieces and nothing else: a model that's good at tool use, a runtime that speaks the Model Context Protocol, a unified data API for the open web, and observability over what the agent actually called. Everything beyond those four is optional. This is the stack we'd build today if we were starting from scratch.

The four pieces

Model — Claude or Codex, whichever your team is closer to. Both are excellent at multi-tool reasoning in 2026, and switching between them is a config change, not a rewrite. Pick the one with the SDK ergonomics you prefer.

MCP runtime — the layer that exposes tools to the model. For development that's Claude Desktop, Cursor, or the Claude Code CLI; for production it's the Agent SDK with a long-lived server holding the MCP connection.

Public-data API — one endpoint surface across the open web. We obviously think UnifAPI is the right answer here (14 live platforms, MCP-native, $0.001 per record), but the architectural point is broader: if you're calling 5 different vendors, you've put the integration work in the wrong layer.

Observability — tool-call traces, token counts, record counts, latency. Without it you can't tell whether a regression is in the model, the upstream, or your prompt.

Model and runtime

The model picks tools; the runtime runs them. In 2026 both Claude and Codex handle multi-step tool use without much prompting — give them a task and a tool list, and they fan out, retry, and synthesize without orchestrator-style scaffolding underneath. The era of LangChain-style hand-rolled tool routers is closing.

For the runtime, the practical question is local-dev experience. Claude Desktop is the fastest iteration loop — every tool call shows up live, MCP servers reload on config change. Cursor is the same once you're inside an editor. The Claude Code CLI is the runtime you'll ship to production via the Agent SDK.

Public-data API layer

The unified data API is the single biggest leverage point in this stack. If your agent ends up calling SerpAPI for search, Firecrawl for scrape, Tavily for answers, and a hand-rolled Reddit wrapper for social, you've built a four-vendor integration problem before you've built the agent.

Push that complexity behind one gateway. UnifAPI's catalog covers social (TikTok, LinkedIn, Instagram, YouTube, Twitter/X, Threads, Reddit, and seven Chinese platforms) with search, scrape, and news on the roadmap — all behind one MCP server. The agent prompt becomes "use the tools to answer X" rather than "call search_api, then if that fails call scrape_api, then if that fails ...".

Observability and replay

Without observability you debug blind. The bare minimum for a production agent is a trace per run that records: the user prompt, every tool call (name, args, response, duration, record count), the model's intermediate reasoning, and the final output. LangSmith, Langfuse, and Helicone all do this competently in 2026; the differentiation between them is now mostly DX.

Once you can replay a run, regressions stop being mysterious. A slow agent run is either a model latency spike, an upstream API slowdown, or a prompt that fans out more than it should — the trace tells you which.

Where this stack still fails

Two known holes. First, OAuth into the user's own SaaS accounts — Gmail, Linear, Notion — isn't in scope for UnifAPI; that's Composio's territory and you bolt it on as a second MCP server. Second, long-running background workflows (overnight crawls, weekly digests) want a scheduler tier above the agent, not inside it; pick a job runner you already trust.

Everything else is in the four pieces above. Resist adding more. The fastest agents in 2026 are the ones with the shortest stack.