The execution plane for agentic systems.
Bring your SOPs, tools, and policies — plug in once. Escepine handles execution end to end.
What breaks when agents hit Production.
It rarely looks like a crash. It looks like plausible wrong output — committed, propagated, and unexplainable at audit time.
Steps that succeed wrongly
HTTP 200. Output plausible. Data wrong. No crash, no retry — the run continues and the wrong answer commits. The documented #1 killer of agent projects.
Hallucination cascades
One fabricated value at step 14 becomes every downstream artifact's truth. By step 40 the report is confidently wrong — and nothing tells you which parts.
Runaway retries
Stale, corrupt, or missing data. The model can't resolve it, so it loops — same prompt, again and again, overnight. You find out from the invoice.
Unauthorized actions
A prompt injection or a confident hallucination fires a real write to a real system. Guardrails living in the prompt die with the prompt.
Silent skips
When work is unresolvable, the agent abandons the run — or quietly skips the step and moves on. Unacceptable when the step was the compliance check.
The audit question
A regulator asks why the agent denied that claim. Spans and log files are telemetry, not evidence. What produced this number, from what input — goes unanswered.
Retries handle steps that fail. Durable execution handles steps that crash. Most of this list is neither.
Errors Compound. Runs Decay.
Serial systems multiply errors — Rsys = (Rstep)n, aerospace math from the 1950s. Drag the slider. Flip the chain.
The 20-step chain
Lusser's Law
A series system's reliability is the product of its parts. Agents are series systems.
Agent reliability decays as
workflow complexity grows.
Probabilistic execution vs. deterministic outcome — same task, both sides.
LLM Control Plane
Escepine
Agents reason. The execution plane owns the outcome.
Why it works/ A dedicated machine for execution.
The model keeps one job. The engine takes the other ten.
Execution Engine
State, schemas, security, retries, routing — executed, not prompted.
Small blocks. Big systems.
One SOP holds prompts, tools, and policies. SOPs call other SOPs. Connect them any way you want. Escepine runs every branch at once.
Trigger SOP
Calls 3 SOPs.
Escepine plugs in here.
Tap any block in the diagram
Spreads out. Runs at once. Comes back together.
Agents fail in known ways. None fail the run.
Caught at runtime — not patched into prompts.
Infinite Loops
Agents get stuck in redundant tool loops — draining API budgets overnight, never finishing.
Execution Bounds
Hard iteration caps and timeouts, enforced by the runtime. Runaway cycles are killed before billing spikes.
Black-Box Runs
Agents fail on step 14, but the intermediate traces are dropped. Root cause: invisible.
Immutable State Ledger
Every state transition and tool payload, logged permanently. Replay the exact run that failed, step by step.
Context Bloat
Tool outputs swell over time, pushing core instructions out of the context window.
Context Pruning
Old outputs are garbage-collected automatically. Core operational rules stay locked in the window.
Cascading Errors
One hallucinated field in step 1 breaks validation five steps later.
Schema Checkpoints
Strict type-checking between every step. Execution halts the moment a shape deviates.
Guardrail Bypass
The model drops safety rules mid-run and touches restricted data.
Runtime Boundaries
Security constraints move out of the prompt and into the runtime. The model can't bypass them — it never sees them.
Shadow Governance
Agents hold standing write-access to production systems, long after the task is done.
Ephemeral Sandboxes · Crypto Identity
Every tool runs in an isolated, temporary container. Each agent gets a cryptographic identity at spawn; all access is revoked the millisecond the run ends.
What changes when Escepine runs your agents.
Six things your team notices in the first month. None of them need a rewrite of your stack.
Runs finish the same way, every time
Same input, same path, same output. No surprises between runs — and no silent detours in the middle.
Lower AI bills
No overnight retry loops. No tokens burned on steps that already passed. Small models can finish work that used to need the expensive ones.
Find the broken step in minutes
Every run keeps a full step-by-step record. Replay the exact run that failed and see where it went wrong.
No standing access to production
Agents get credentials when a run starts. Access is gone the moment the run ends. Nothing left for an attacker to steal.
Swap models, keep workflows
Your workflow logic lives in the runtime, not in a prompt. Change the model underneath — the workflow doesn't notice.
Long runs stay healthy
Hour 4 looks like minute 1. Memory stays clean, context stays tight, and speed doesn't fall off a cliff.
None of this is a roadmap. Every line above is enforced by the runtime today.
Make your agentic systems reliable in Production
We're onboarding the first teams now.
Early teams get a direct Slack line to the founders — and priority onboarding when their slot opens.