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Python, TypeScript, and Go SDKs are ready for production

The reliability platform for AI agents and workflows

We don’t just watch agents. We run them.

Set up AGNT5 with your coding agent:Build with AI

AGNT5 runs your agents in production, records every step, and lets you test fixes against real failures, so you can ship the next version with confidence. Fully managed, or in your own cloud.

Why agents fail

Agents fail in ways your tools can’t see. A tool call errors out, every step succeeds and the answer is still wrong, or the server dies mid-run.

  1. 01A tool call errors out[ in the trace ]
  2. 02Every step succeeds, the answer is wrong[ only if your evals cover it ]
  3. 03The server dies mid-run[ not in the trace ]

Your tracing tool sees the calls it was told about. Your eval tool sees your test set. Your job queue sees the retries. None of them sees the whole run.

  1. 01 A tool call errors out

    The loud failure. An exception, a timeout, a bad response.

  2. 02 Every step succeeds, the answer is wrong

    The silent failure. Nothing errors, the output is still bad.

  3. 03 The server dies mid-run

    The infrastructure failure. The run just stops halfway.

Today vs AGNT5

Debugging an agent today means stitching the run back together.

The trace is in one tool, retries in another, eval scores in a third, and a crash usually shows up as an infra alert, not in the run. AGNT5 runs your agent, so the whole run is one record.

Before
After

How it works

One loop, from first run to proven fix. Build agents that survive failure, run them with a verdict on every deploy, and improve them on the runs that failed.

Crash on step 5? The run resumes at step 5. Reliability comes with the SDK: steps are checkpointed, failed calls retry, and a run can wait for people. In Python, TypeScript, or Go.

from agnt5 import function, workflow, FunctionContext, WorkflowContext

@function(retries=3, backoff="exponential")
async def issue_refund(ctx: FunctionContext, order_id: str) -> str:
    ...  # flaky payment API: retried on failure

@workflow
async def refund_order(ctx: WorkflowContext, order_id: str) -> dict:
    order = await ctx.step(fetch_order, order_id)  # checkpointed

    decision = await ctx.wait_for_user(  # can wait hours or days
        question=f"Refund {order['total']}?",
        input_type="approval",
        options=[{"id": "approve", "label": "Approve"},
                 {"id": "reject", "label": "Reject"}],
    )
    if decision == "reject":
        return {"status": "declined"}

    await ctx.step(issue_refund, order_id)  # never runs twice
    return {"status": "refunded"}

Flaky tools, APIs, and models.

Failed calls retry with backoff. Steps that already succeeded don’t run again.

Waiting on people.

A run can pause for an approval for hours or days, then carry on from that step.

Every deploy gets a verdict: better, worse, or no change. Deploy agents like any other service. After each deploy, AGNT5 compares the new version with the last one on live runs.

Deployment verdict[v14] → [v15]
  • Error rate−45%
  • p95 latency−17%
  • Cost per run−9%
  • Eval score+25%
Compared on 1,240 live runs since deploy

Safe rollouts.

Preflight checks before a deploy. Promote or roll back in one step.

Fleet health.

Runs, errors, latency, and cost across every deployment, with workers that scale with load.

Replay the runs that failed. Ship when the fix beats them. AGNT5 groups failures into issues with the runs that show them, replays those runs against your change, and scores old against new.

Issue · open38 runs

Tool timeouts in search_api

38 runs affected · since deploy [v15]

Likely causesearch_api responses slowed past the tool timeout after the deploy.
  • run 7f3a · step 3failed
  • run 2c91 · step 3failed
  • run e04b · step 3failed
  • + 35 more runs

Fix it on the real inputs.

Replay failed runs with the same inputs and tool responses. Change only the prompt, model, or code, and save them as tests.

Prove it before it ships.

Score the new version against the old one on the same runs. Ship when it’s better.

Every fix ships back to Run, where the deploy gets its verdict.

Bring the agents you already have.

No rewrite. Wrap your agent’s entry point in an AGNT5 function and call your framework exactly as you do today. Your prompts, tools, and model providers stay as they are.

  1. Step 1

    Install the CLI

    curl -LsSf https://agnt5.com/cli.sh | bash

    For Python, TypeScript, or Go projects.

  2. Step 2

    Wrap your existing agent

    @function # on your entry point

    One decorator. The agent code inside it doesn’t change.

  3. Step 3

    Deploy

    agnt5 deploy

    Your first run is recorded, start to finish.

Works with

OpenAI Agents SDKClaude Agent SDKGoogle ADKVercel AI SDKCloudflare Workers

Deployment

Run it in our cloud or yours.

Use AGNT5 fully managed, or run it inside your own environment. If prompts, outputs, and customer data can’t leave your network, they don’t have to.

Talk to us about your cloud
AGNT5 control plane
Command centerDeployments
Your network
Your agentsRuntimeRun recordsPromptsOutputsCustomer data
  • Fully managed

    Sign up and deploy. We run everything.

  • In your cloud

    Your agents run, and your data stays, in your environment.

  • Same features either way

    Build, Run, and Improve work the same in both, deploy verdicts included.

Deploy your first agent in 5 minutes.

Start free with 250K steps a month.