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Ship AI agents that don’t break in production.

Test, monitor, and improve agents before real users find the bugs

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Live in production

Every agent run, on one timeline.

Open any trace and see the exact prompt, the tool calls it made, the retries it hid, and where the latency actually went.

trace · 8f2c1a9b
1.24s 12 spans
8f2c1a9b
1.24s
d9e2a14f
0.86s
c8a21b3d
2.41s
f4e5d6c7
1.02s
a1b2c3d4
0.74s
e5f6a7b8
1.55s
c9d0a1b2
0.93s
b3c4d5e6
1.18s
SPAN START DURATION
agent_run
1240ms
intent_parse
90ms
db_retrieval
210ms
guardrail_in
30ms
llm_reasoning
720ms
tool_call_01
60ms
retry_01
30ms
db_retrieval_2
120ms
tool_call_02
60ms
guardrail_out
30ms
token_count
25ms
output_format
45ms

Three surfaces, one pipeline.

Instrumentation, evaluation and remediation share one trace format, so a failure you see in production is the same object you test against.

Observe

Every production trace scored the moment it completes — tool errors, loops, refusals, latency cliffs and cost spikes, surfaced as they happen rather than at month end.

  • Automatic span capture
  • Streaming score computation
  • Alert on distribution shift
TIME EVENT STATUS LATENCY
14:02:11 tool.timeout
ERROR
 12.4s
14:02:08 llm.refusal
FAIL
 840ms
14:01:55 context.truncated
WARN
 1.2s
14:01:42 retry.exhausted
ERROR
 6.6s
14:01:30 span.completed
PASS
 92ms
14:01:12 db.reconnect
WARN
 2.1s
14:00:58 tool.success
PASS
 45ms
14:00:44 agent.start
PASS
 12ms

Built for the failure modes agents actually have.

run_8f2c
PASS
+12%
run_d9e2
PASS
+4%
run_c8a2
FAIL
-2%
run_f4e5
PASS
+8%
run_a1b2
PASS
+1%

Regression gates

Every pull request replays yesterday's production traffic. A regression blocks the merge before it blocks a user.

tool_timeout4,102
context_overflow2,887
refusal_loop1,344
auth_error901
rate_limit512

Failure clustering

Ten thousand broken traces collapse into nine root causes, ranked by how many users each one touched.

v1.4.2 → v1.5.0
def query_engine():
- temp = 0.7
+ temp = 0.2
return llm.call()
! retry_limit = 3

Version replay

Re-run any slice of production traffic against any build. Diff the outcomes span by span.

One import. Everything after that is automatic.

Two lines to instrument. No sampling config, no manual spans, no agent-framework lock-in.

pip install shipagents from shipagents import trace
@trace
def support_agent(query: str) -> str: plan = llm.plan(query) docs = retriever.search(plan.terms) return llm.answer(query, docs)
openai
anthropic
google-genai
langgraph
llamaindex
crewai
vercel-ai
mastra
pydantic-ai
dspy
autogen
semantic-kernel
2.1 M
traces scored per day
38 ms
p99 instrumentation overhead
94 %
of regressions caught pre-release
11 min
median time to root cause
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Find the bugs before your users do.

Ten minutes to first trace. No credit card, no sales call.

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