WHAT CAUSES THIS?
Why it breaks in production
Happy-path demos hide partial failure and retry semantics.
- Long-running work loses context or credentials.
- Side effects are not idempotent.
- Nobody owns the exception queue when confidence is low.
problem_kicker
“Can this run every day without someone watching it?”
A workflow that succeeds while an engineer watches the demo is not yet automation. Daily operation means missing inputs, expired credentials, changed APIs, ambiguous cases, retries and partial side effects must be expected rather than treated as surprises.
AI workflowsagentsorchestrationobservability
DEMAND LANGUAGE / REAL-WORLD PROBLEM
Does this sound familiar?
“The demo works. Will it still run every day when nobody is watching?”
“What happens when data is missing, an API is down or a run stops halfway?”
architecture_for RELIABLE UNATTENDED AI WORKFLOWS
engineering
We design the failure path before scaling the happy path. Every durable step has explicit state, retry policy, timeout, ownership and observable post-condition.
authority
Credentials are scoped to the step and lifetime required. Approval gates are attached to consequences, not to arbitrary model confidence thresholds.
critical
Throughput, queue age, retry amplification and human intervention rate matter more than a single model response time.
vendor
AI workflows · agents · orchestration · observability
failure_kicker
anti_title
- Infinite retries after a non-idempotent write.
- Silent fallback that changes business meaning.
- No durable workflow state.
- No human queue for ambiguous or high-impact cases.
measure_kicker
verify_title
verify_intro
- Successful completion rate without manual supervision.
- Duplicate/partial side-effect rate.
- Mean time to detect and recover from failed steps.
- Exception queue age and intervention rate.
CTO / CIO FAQ
faq_title
How autonomous should the workflow be?
As autonomous as the consequences and verification allow. Irreversible or ambiguous actions may still need approval.
What happens when an API is down?
The workflow should preserve durable state, apply bounded retries and resume or escalate without duplicating side effects.
How do we know it is production-ready?
Run failure injection, replay and soak tests against realistic workloads and dependencies.
connected