THE CONTROL PLANE FOR AGENTIC FLEETS.
Orchestrate, observe, and govern production AI agents with instrument-grade precision. Stop guessing execution flow.
// TRUSTED BY ENGINEERING TEAMS SHIPPING AGENTS IN PRODUCTION
AI AGENTS ARE BLACK BOXES.
Your agents work in demo. They fail silently in production. You can't see the reasoning, can't cap the spend, can't roll back a bad prompt. AgenticPlane gives you the instrumentation to move from "it works on my laptop" to enterprise infrastructure.
Stateful execution
Durable agent loops with checkpointing, retries, and branching. Pause for human approval mid-run, resume hours later — no glue code.
Trace waterfall
Every reasoning step, tool call, and token costed and indexed. Replay any production run. Filter across millions of agent sessions.
Policy engine
Declarative guardrails for tool access, PII, and spend. Per-agent budgets, approval gates, audit logs your CISO will sign off on.
Continuous evals
Backtest prompt and model changes against production traces. Catch regressions before they ship. CI for stochastic systems.
TEN LINES TO FULL VISIBILITY.
Drop the SDK in front of any LLM provider. Zero latency overhead. Full OpenTelemetry compatibility. Works with the agent framework you already use.
- [+]PYTHON & TYPESCRIPT NATIVE
- [+]MODEL-AGNOSTIC: OPENAI, ANTHROPIC, OPEN MODELS
- [+]BUILT-IN RETRY, FALLBACK, CIRCUIT BREAKERS
- [+]OPENTELEMETRY EXPORTERS OUT OF THE BOX
from agenticplane import Plane, policy plane = Plane(api_key="ap_8f2x...") @plane.agent(id="finance.classifier") async def run(ctx, prompt: str): result = await ctx.reason( model="gpt-4o", tools=[sql_query, http_get], policy=policy.READ_ONLY, budget_usd=0.50, ) return result.summary
YOU COULD BUILD THIS. DON'T.
Every team shipping agents rebuilds the same plumbing. Skip the eighteen months of undifferentiated infrastructure work.
READY TO INITIALIZE?
Get your agents production-ready in an afternoon. Free up to 10k traces/month — no card.