Coding Agent Environments
Give every coding agent an environment to test its own code.
Coding agents write code in minutes, but with nothing real to run it against, their bugs wait for staging. A coding agent environment is an ephemeral environment where the agent runs its change against the real services, databases and message queues it depends on, then fixes what breaks before it opens a pull request. Signadot creates one for every task on the Kubernetes cluster you already run.
- Works with Claude Code, Cursor and Codex through MCP
- Real databases, queues and workflows in every environment
Trusted by engineering teams worldwide
Agents write the code. Staging finds the bugs, days later.
Without an environment of its own, a coding agent can only test the code it generates against mocks. Integration bugs surface after the pull request reaches staging, and every fix means rework: back to the agent, back through review, back to staging.
Unit tests only
On a laptop or in a sealed cloud sandbox, the agent has no real services to call. It checks functions in isolation and never sees how its change behaves across the system.
One staging for every agent
A shared staging cluster can't host every developer and every agent at once. Agents wait in line, or test on top of each other's changes and break each other's results.
Late bugs mean rework
A bug found in staging sends the change back to the start, long after the agent moved on. Reviewers become the test layer, and the time saved writing code is spent fixing it.
Ephemeral environments for coding agents
The best ephemeral environments for coding agents give every task its own copy of what the agent changed, connect it to real versions of everything else, and let the agent create and delete it from its own tools in seconds. Signadot does that on the cluster you already run, so thousands of agents test in parallel without copying the stack.
Where a coding agent can test its change, compared by approach
| Approach | Real services and data | Agents in parallel | Ready in | Finds integration bugs before the PR |
|---|---|---|---|---|
| Signadot ephemeral environment | Yes: the shared services and data on your cluster, kept apart per task | Thousands on one cluster | Seconds | Yes, in the agent's own task |
| Full copy of the stack per task | Yes, a copy that drifts from the original | As many as the budget allows | Minutes | Yes, at the cost of a stack per agent |
| Shared staging cluster | Yes | One change at a time | After a deploy and a queue | After the merge |
| Code-execution sandbox (container or microVM) | Only what the agent installs in it | Many | Seconds | Rarely: no services to call |
| Developer's laptop or devcontainer | Mocks, or the few services that fit | One per machine | Seconds | Rarely |
Your coding agent closes the loop.
The agent writes, runs, tests and debugs its change against real dependencies until the tests pass, without waiting on a person. It works on its own for longer, and you get a pull request that has already been tested end to end.
1. Create an ephemeral environment
The agent asks Signadot for an ephemeral environment through the MCP server or the CLI. In seconds, the service it is about to change is wired to the real services, databases and queues in your cluster.
2. Write the change
The agent edits the service, the same way it would anywhere else.
3. Build and run
The agent runs the service on its own machine. Requests that carry the environment's routing key reach that process, and its own calls go out to real services in the cluster.
4. Run integration and end-to-end tests
Your tests run against the environment and hit real services, not mocks. The agent can also run a Signadot plan, a saved test workflow it picks to match the diff.
5. Debug and repeat
When a test fails, the agent reads the logs, inspects the request that failed, fixes the code and runs the tests again. Once they pass, it opens a pull request with the results.
$ signadot local connect
# cluster service names now resolve on this machine
$ signadot sandbox apply -f pr-431-payments.yaml \
--set cluster=staging
# local: Deployment payments-svc → localhost:8080
✓ sandbox pr-431-payments ready in 2.1s
payments-svc your machine
api-gateway baseline
auth-svc baseline14 func HandlePayment(w http.ResponseWriter, r *http.Request) {
15 key := r.Header.Get("Idempotency-Key")
16 + if cached, ok := store.Get(key); ok {
17 + json.NewEncoder(w).Encode(cached)
18 + return
19 + }
20 + if store.IsExpired(key) {
21 + http.Error(w, "key expired", 409)
22 + return
23 + }
24 result := processPayment(r)
25 store.Set(key, result, 24*time.Hour)
26 }$ eval $(signadot sandbox get-env pr-431-payments)
# payments-svc's cluster environment, exported locally
$ go run ./cmd/payments
[info] listening on :8080
[info] connected → postgres.db.svc:5432
[info] connected → redis.cache.svc:6379
[info] connected → kafka.bus.svc:9092
● ready: requests with the sandbox's routing key reach this process$ signadot job submit -f e2e-job.yaml \
--set sandbox=pr-431-payments --attach
# playwright test, routed to the sandbox's payments-svc
Running 3 tests using 3 workers
✓ checkout.spec.ts › completes checkout (2.1s)
✓ auth.spec.ts › signs in (1.4s)
✘ payment.spec.ts › rejects an expired key (0.7s)
POST /payments (expired key) returned 500
Expected: 409 Conflict
1 failed, 2 passed (4.2s)◆ Inspecting the failing request from payment.spec.ts...
→ tail -f logs/payments-svc | grep idem
10:42:01 store.Get(key) → nil, ttl=expired
10:42:01 processPayment() called, missing IsExpired() guard
→ signadot traffic record --sandbox pr-431-payments
req POST /payments Idempotency-Key: k_842 (expired)
res 500 internal server error
◆ Root cause: expiry check runs after processing. Reordering checks and looping back.How it works
Only what changed runs in each environment.
Signadot doesn't copy the stack for each task. It deploys only the services the agent changed, or the components a task needs to itself, and wires them into the rest of your cluster: request routing for synchronous calls, message routing for queues and batch work, and configuration for services that should point at the new version. To the agent, it looks like an entire environment of its own.
- One cluster, thousands of environments. Agents test in parallel on the cluster you already run, with no queue for staging.
- Each agent sees only its own change. Two agents editing different services never pick up each other's work.
- Ready in seconds. No rebuild of the whole stack and no new cluster for each task.
- Gone when the task ends. Environments expire on a timer, so idle agents leave nothing running.
83% lower change failure rate
We basically stopped creating full preview environments and replaced our custom solution with Signadot. Instead of isolating the full environment, the strategy using routing keys is much lighter, and we are able to provide an isolated environment, even with isolated databases, per PR quite fast.
Marcus TavaresStaff Software Engineer Bitso's coding agents test every change in its own environment.
With pull requests per developer up 60% as coding agents took on more of the work, Bitso moved 250+ engineers and their agents to ephemeral environments on one cluster and cut its change failure rate by 83%.
Same prompt. Very different outcomes.
Two agents get the same task. One checks its work with unit tests and a linter. The other tests against live services in an ephemeral environment and catches three broken consumers before anyone reviews the code.
Ships broken consumers Unit tests pass, but three downstream services break on the schema change. The agent never sees it. Staging does, days later.
Ships tested code Caught three consumer breakages and kept fixing until every downstream service passed. The pull request is ready for review.
An agent's tests are only as good as the dependencies behind them.
A test that runs against mocks passes whatever the code does. Every Signadot environment reaches real databases, message queues, and batch and workflow systems, with each task's data and messages kept apart, so an agent's tests catch the failures that would otherwise surface in staging.
-
Databases
Share the existing database with each task's rows kept apart, or give a schema change its own temporary database or Neon branch, created with the environment and deleted with it.
Data isolation -
Message queues
Kafka, RabbitMQ, NATS, SQS and SNS, and Google Pub/Sub stay shared. Messages carry the task's routing key, so the agent's consumer reads its own messages and nobody else's.
Message queue isolation -
Workflows and batch jobs
A changed Temporal worker polls the shared task queue next to the stable one, and the workflows the agent starts run on its new code. Async and batch work follows the same routing key.
Temporal tutorial -
Resource plugins
Anything else the task needs, created when the environment starts and deleted when it ends: a managed database, a topic, a storage bucket, even a virtual machine outside Kubernetes. Written with Helm, Terraform or a script.
Resource plugins
Native MCP server
Connect your coding agent through MCP.
The Signadot MCP server gives Claude Code, Cursor, Codex and any other MCP client the operations it needs to create and use ephemeral environments, in plain language. The same operations are in the Signadot CLI, and agent skills such as signadot-validate tell the agent when to use them: create an environment, run the right tests, read the failure, try again.
Environments Create, update and delete ephemeral environments, add the services a task changes, map them to local processes and get preview URLs.
Discovery List clusters, find workloads and service endpoints, and browse the resource plugins an environment can use.
Routing Group environments into route groups, so a change that spans several services or tasks is tested together.
Whichever agent you pick, Signadot is the environment.
Every major coding agent reaches the same environments through MCP or the CLI. No per-agent integration and no lock-in.
Bring your own agent In-house agents connect through the same MCP tools or CLI, and background agents running in cloud sandboxes connect with an API key, as the Modal example shows.
Coding Agent Environments FAQ
What is a coding agent environment?
It's where an AI coding agent runs and tests the code it writes: the service it changed, the real services and data stores that service calls, and the routing that keeps the agent's test traffic apart from everyone else's. With Signadot, each agent task gets one as an ephemeral environment on your Kubernetes cluster, ready in seconds and deleted when the task is done.
What are the best ephemeral environments for coding agents?
Look for four things. The agent creates and deletes environments from its own tools (an MCP server, a CLI or skills), each environment reaches real services and data instead of mocks, many run in parallel on shared infrastructure, and each one starts in seconds. Signadot does all four by duplicating only what a task changed on the cluster you already run. Tools that copy the whole stack per environment also give real services, but every agent then costs a full stack and minutes of startup.
How is a coding agent environment different from a code-execution sandbox?
A code-execution sandbox is a container or microVM where the agent itself runs, isolated from your systems for safety. A coding agent environment is what the agent tests against: your real services, databases and queues, with its change wired in. The two work together. An agent can run in a cloud sandbox and connect to a Signadot environment to test, as the Modal example on GitHub shows. Signadot's product calls its environments Sandboxes, but they solve the second problem, not the first.
How does a coding agent test against real databases and message queues?
The environment reaches the databases and brokers your services already use, with each task's data kept apart. Messages on Kafka, RabbitMQ, NATS, SQS and Google Pub/Sub carry the task's routing key, so the agent's consumer reads only its own. A schema change can get its own temporary database or Neon branch, and resource plugins create anything else a task needs, inside or outside Kubernetes.
How do I connect Claude Code, Cursor or Codex?
Install the Signadot CLI, which includes the MCP server, and add the server to your agent's MCP settings, or let the agent run the CLI directly. Then add the Signadot agent skills, which tell the agent when to create an environment, which tests to run and how to read a failure. Any agent that speaks MCP or can run a shell command works, in-house agents included.
Is this an alternative to staging for AI-generated code?
It replaces the queue for staging, not the cluster. Ephemeral environments run on the staging or pre-production cluster you already have and share its services and data. Each agent tests its AI-generated code in an environment of its own before the pull request, so a bug is caught in the agent's task instead of after the merge, and staging stays stable for everyone else.
How many coding agents can test at the same time?
Thousands, on one cluster. Each environment deploys only the services its task changed, plus any database or queue the task needs to itself, and shares everything else. An environment costs a few pods, not a copy of the stack, and it expires on a timer so idle agents leave nothing behind.
Can a platform team control what agents do?
Yes. Agents act through service accounts with admin, member or viewer roles, and every change lands in the audit log. Signadot runs in your cluster, and by default its control plane receives only resource metadata such as workload names and status, never your source code or application data.
New to the model? Read what coding agents change about ephemeral environments, our guide to validating AI-generated code on Kubernetes, and, for running agents across the whole delivery pipeline, the complete guide to software factories.