What actually breaks when agentic workflows reach production
Demos tolerate a 70% success rate. Production does not. The failure modes we see most often, and the guardrails that contain them.
An agent that plans, calls tools and verifies its own work is straightforward to demo and genuinely difficult to operate. The gap is not model quality. It is everything around the model: how failure is detected, how state is recovered, and who is accountable when a run does the wrong thing convincingly.
Failure compounds across steps
A single step at 95% reliability looks excellent. Chain eight of them and you are at 66%. Most teams discover this after launch, because evaluation happened per-step rather than per-workflow. Measure the trajectory, not the token.
- Score whole runs against a golden set of real tasks, not isolated prompts.
- Track a step-budget per run and fail loudly when it is exceeded, rather than looping.
- Treat a tool call that returns an unexpected shape as a hard error, never as free-text to reinterpret.
Checkpoints beat retries
Blind retries multiply cost and side effects. Durable checkpoints after each externally visible action let a run resume from the last known-good state, which matters enormously once agents write to systems of record rather than just reading from them.
If an agent can take an action you would not let a new joiner take unsupervised, it needs a human checkpoint — not a better prompt.
Put a boundary around the blast radius
Scope credentials per run, not per service. Make every write idempotent so a replay is safe. Log the full plan, the tool inputs and the observed outputs so a run can be audited months later by someone who was not there.
None of this is exotic. It is the same operational discipline any distributed system earns — applied to a component that fails in unusually plausible-looking ways.
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