Hey everyone, We built Escepine because while LLMs are incredible at reasoning, they make for terrible, unreliable control planes in production. If you’ve built an agentic workflow longer than 10 steps, you’ve likely watched Lusser’s Law destroy your run reliability—errors compound, context bloats, and agents drift into infinite loops that drain your API budget overnight. Traditional guardrails live inside the prompt, which means they die with the prompt. Escepine introduces a decoupled paradigm: the model keeps one job (planning/reasoning), and our agentic execution engine handles the other ten (state, schemas, security, boundaries, and routing). We plug into your existing stacks—whether you're using LangGraph, Temporal, LiteLLM, or E2B—to enforce deterministic outcomes from non-deterministic inputs. How we handle runtime enforcement at the execution layer: • Execution Bounds: Hard iteration caps and timeouts enforced by the runtime, stopping runaway API cycles before billing spikes. • Context Pruning: Automatic garbage collection of old tool output tokens to prevent instruction drift. • Immutable State Ledgers: A complete step-by-step evidence record to replay and debug silent skips or hallucination cascades instantly. • Ephemeral Sandboxes: Isolated, temporary containers with cryptographic identities that revoke access the millisecond a run ends. We are onboarding our first few teams right now. We would love to hear from the engineers here: What is the biggest failure vector breaking your agent chains when they transition from staging to production? Let’s talk architecture!

Escepine

The execution plane for mission critical agentic systems

The execution plane for mission critical agentic systems