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Your AI agent can call Claude Code. But can it do that at 3 AM without crashing, hanging, or silently failing? I built a Bridge middleware that handles process management, timeouts, and automatic recovery -- so my 10 agents run Skills overnight while I sleep.
TL;DR -- Your AI agent can call Claude Code. That part is easy. The hard part is making that call survive the night: handling crashes, timeouts, missing parameters, and the dreaded "please enter..." prompt at 3 AM with nobody at the keyboard. I built a three-layer middleware called Bridge that manages the entire lifecycle of a Skill execution, from process spawning to result delivery. After two months of daily production use with 10 agents, here is the architecture.
If you are building AI agent automation -- not chatbot conversations, but agents that actually do work -- you have probably hit this wall already.
On one side, you have Claude Code running on a subscription plan. Manually, it is fast, stable, and genuinely productive. On the other side, you have an orchestration framework with multiple AI agents assigned to specific roles: research, content creation, SEO analysis, image generation, publishing.
The problem is connecting these two sides reliably.
Your agent can start a Claude Code session and tell it to run a Skill. That much works. But what happens next? Did the parameters get passed correctly? What if the process hangs for 40 minutes? If it crashes, does the agent even know? I ran my system for three straight days before discovering that half my overnight tasks had died silently. The agents thought they were still running. The processes were long gone.
Being able to call something is not the same as being able to call it reliably.
I was wrong about how simple this would be. I assumed that once I could trigger a Skill programmatically, the hard part was over. In reality, the invocation was maybe 10% of the work. The other 90% was building everything around it: process monitoring, crash recovery, timeout enforcement, parameter pre-packaging, and result delivery.
That 90% is what Bridge does. It turns fragile Claude Code Skill invocations into reliable, automated operations. The Claude Code documentation covers the Skill system and SDK capabilities; the Anthropic developer docs provide the API reference that the Agent SDK builds on.
Bridge is not a framework or a library. It is a middleware layer that sits between your agent orchestrator and Claude Code, managing the full lifecycle of every Skill execution.
Here is what happens when an agent wants to run a Skill:
Without Bridge, Claude Code Skill automation is just an agent firing a command into the void and hoping for the best. With Bridge, every execution is tracked, every failure is caught, and every result finds its way home.
I tried building this as a single script first. It was 500 lines of mixed Bash and Python, and when it failed at 2 AM, the logs were useless. Every error looked the same. I could not tell whether the crash was in process management, environment setup, or the actual SDK call.
That night taught me a principle I now follow everywhere: each layer should do exactly one thing, so when something breaks, you know which layer broke.
This is the entry point. When an agent wants to execute a Skill, it calls the Dispatcher. The Dispatcher handles:
Think of this as a front desk at a factory. A client walks in with an order. The front desk logs the order, assigns a tracking number, and decides whether the client should wait or come back later. If the order fails, the front desk updates the client.
Before any code runs, the Gatekeeper verifies the execution environment:
If any check fails, the Gatekeeper stops execution immediately and returns a specific, actionable error message. No vague "something went wrong" -- it tells you exactly what is missing and how to fix it.
I added this layer after spending an entire morning debugging what turned out to be a missing virtual environment activation. The actual error was buried 200 lines deep in a Python traceback. With the Gatekeeper, that same failure now produces a one-line message: "venv not found at /path/to/venv -- run uv sync to create it."
This is where the real work happens. The Executor uses the Claude Agent SDK's query() function to:
BaseException for signals like SIGTERM
The separation matters. When I see an error in the Executor logs, I know the environment is fine and the issue is either in the SDK call or the Skill itself. When I see an error in the Gatekeeper logs, I know the code never ran and the issue is environmental. When I see an error in the Dispatcher logs, I know the issue is in task routing or state management.
Three layers. Three possible failure points. Zero ambiguity about where to look.
Bridge supports two execution modes, and choosing the wrong one will either waste your agent's time or lose track of long-running tasks.
The agent sends a request and waits. The Dispatcher blocks until the Skill completes, then returns the result directly. This works for quick tasks: looking up a value, sending a notification, running a short calculation. Anything under 30 seconds is a good candidate.
The downside is obvious. While waiting, the agent cannot do anything else. If you have 10 agents and each one is stuck waiting on a Skill, your entire system grinds to a halt.
The agent sends a request and gets back a tracking reference (a path to the state file) immediately. The Skill runs in the background. When it finishes, Bridge delivers the result to the agent through a callback.
In my setup, 90% of Skill executions use async mode. Most of the work my agents do -- writing articles, generating images, running SEO analysis, producing video scripts -- takes anywhere from 3 to 30 minutes. Blocking an agent for that long defeats the purpose of having multiple agents.
Here is the practical difference. With sync mode, 10 agents process 10 tasks sequentially. With async mode, 10 agents fire off 10 tasks simultaneously and pick up the results as they come in. For a solo operation, async mode is not a nice-to-have. It is the only way the math works.
Every execution writes a state.json file that records:
starting, running, done, error, killedThe agent can check this file at any time to see how a task is progressing. But here is the detail that took me weeks of debugging to get right: Bridge also validates whether the process is actually alive. If the PID in the state file no longer corresponds to a running process, Bridge automatically corrects the status to killed -- even if the process was terminated by SIGKILL, which cannot be caught by any signal handler.
I added that check after discovering a state file that said running for a task that had been dead for six hours. The operating system had killed the process due to memory pressure, and without PID validation, nobody knew.
After two months of running automated Skills, I learned one lesson the hard way:
The worst thing a task can do is not fail. It is disappear.
Failure is fine. You get an error message, you fix the issue, you retry. But disappearance? The task crashes, no notification goes out, the agent thinks everything is still running, and you wake up the next morning to discover that nothing happened overnight.
Bridge has three layers of protection against this.
No matter how a task ends -- normal completion, unexpected crash, forced termination -- the Dispatcher always writes a final state record. This is implemented as a shell EXIT trap, which fires on any exit, including crashes. The state file captures what happened and when.
The Executor catches BaseException, not just Exception. This means it handles keyboard interrupts, system exit signals, and even memory errors. When any of these fire, the Executor does three things before dying: writes the error state, logs the stack trace, and triggers a notification to the agent.
Every task gets a configurable timeout. If a Skill exceeds its time limit -- maybe it is deadlocked, caught in a loop, or waiting for input that will never come -- the watchdog terminates the process, writes a timeout status to the state file, and notifies the agent.
These three layers stack. The watchdog catches hung processes. The exception handler catches crashes. The EXIT trap catches everything else, including cases where the operating system terminates the process externally.
The design philosophy comes from Nassim Taleb's concept of antifragility: a good system does not try to prevent all failures. It ensures that every failure is detected, recorded, and surfaced. When every failure teaches the system something, the system gets more reliable over time.
Claude Code sometimes pauses mid-execution to ask a question: "What is the target language?" or "Please provide the file path."
When you are sitting at your desk, you type an answer and move on. But Bridge runs at 3 AM with nobody at the keyboard. An unanswered prompt means the Skill sits there doing nothing until the watchdog kills it -- and you have wasted the entire execution window.
The solution is parameter pre-packaging. When the agent dispatches a task, it includes all the information the Skill might need: configuration values, file paths, context about the current project, answers to likely questions. Claude Code receives this as a structured prompt and finds what it needs without asking.
For the edge cases where something is still missing, Bridge intercepts the interactive prompt through a PreToolUse hook on AskUserQuestion. Instead of waiting forever, Bridge logs the question, stops the execution, and reports back to the agent: "Claude Code asked this question. Provide the answer and re-dispatch."
The agent fills in the gap and tries again. No infinite waiting. No wasted hours.
I learned this the hard way during my first week of overnight automation. Every morning, I would find 3 or 4 Skills frozen on a "Please enter..." prompt. They had been sitting there since midnight, burning through my execution window, producing exactly nothing.
The biggest enemy of automation is not technical difficulty. It is unexpected human-in-the-loop moments.
The last piece of the loop: when a Skill finishes, the result needs to reach the right agent in the right place.
Bridge handles delivery through a callback mechanism. When the Executor completes, it triggers a delivery command that routes the result to the originating agent. The agent knows which channel or workspace to report back to, and it posts the result there.
Here is what a typical overnight cycle looks like:
In the morning, I open Discord with a coffee and find the SEO report ready, the blog draft written, the image assets generated, and the subtitle translations completed. Ten agents, each reporting their results. All I need to say is "Looks good."
That is the difference between using AI for chat and using AI as a workforce.
You might ask: what happens if Bridge itself crashes? The EXIT trap ensures the state file gets updated even in that scenario. The next time the agent checks, it sees the abnormal termination and can choose to retry. Reliability does not mean nothing ever breaks. It means every break gets noticed.
Here is how all the pieces fit together:
| Component | Responsibility | Failure mode |
|---|---|---|
| Agent Orchestrator | Decides which Skill to run, with what parameters | Logs decision and dispatches |
| Dispatcher (bridge-runner.sh) | Creates run directory, manages state, selects sync/async | EXIT trap writes final state |
| Gatekeeper (bridge.sh) | Validates environment before execution | Returns actionable error message |
| Executor (bridge.py) | Runs Claude Agent SDK, streams responses, maintains heartbeat | BaseException catch + error state |
| Watchdog | Enforces timeout limits per task | Terminates process + writes timeout state |
| State File (state.json) | Single source of truth for task status | PID validation detects zombie states |
| Callback Delivery | Routes results back to originating agent | Agent retries on delivery failure |
The entire system follows one principle: separation of concerns with guaranteed state persistence. Every component does one thing. Every transition writes state. No Claude Code Skill execution can vanish without a trace.
Copy this prompt into Claude Code to scaffold the Bridge system from scratch:
Build a Bridge system for automating Claude Code Skill execution from an AI agent orchestrator.
Requirements:
1. Three-layer architecture:
- bridge-runner.sh: process management, state tracking, sync/async mode selection
- bridge.sh: virtual environment activation, dependency validation
- bridge.py: Claude Agent SDK query() invocation, message stream processing
2. State management:
- Each execution creates an isolated run directory
- state.json tracks status (starting/running/done/error/killed), PID, timestamps, heartbeat
- Atomic writes using tmp + rename to prevent partial reads
- PID validation to detect processes killed by SIGKILL
3. Three-layer fault tolerance:
- Shell EXIT trap for guaranteed cleanup on any exit
- Python BaseException handler for crashes, signals, and OOM
- Configurable watchdog timer with automatic process termination
4. Interactive prompt handling:
- PreToolUse hook intercepting AskUserQuestion
- Parameter pre-packaging in the dispatch payload
- Graceful stop + report on unanswered prompts
5. Communication:
- stdout markers: [BRIDGE:STATUS], [BRIDGE:DONE], [BRIDGE:ERROR]
- Callback delivery to originating agent on completion
6. Configuration:
- Centralized config.py for model, timeouts, heartbeat intervals, Skill directories
- Security rules: no full-disk search, no sensitive directory access, read SKILL.md first
Stack: Python 3.12+, uv package manager, Claude Agent SDK
This gives you the scaffolding. The engineering decisions -- timeout values, retry policies, which Skills to run async -- those come from running the system and watching what breaks.
Here is what I would tell myself before starting:
Most people building with AI agents are still at the "Can I make it do something?" stage. The shift to "Can I make it do something reliably, overnight, without me?" -- that is where the real engineering starts.
Building Bridge was the moment my Claude Code Skill automation stopped being a demo and started being infrastructure. It is not glamorous work. But without it, ten agents are just ten expensive processes waiting to crash.
— Leo
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