Each example below follows the same structure: the workflow, the real win when it works, and the recurring failure mode, with the architectural control that prevents it. Back up to the agentic AI pillar for the framing behind this list.
Connected-vehicle dispatch
Automotive · Service
Workflow. A vehicle reports a fault. Diagnostic, customer, logistics, routing, and communication agents coordinate to schedule service, confirm eligibility, and notify the customer.
The win. End-to-end resolution without a human touching the ticket queue.
Failure mode. Upstream misinterpretation of the fault code propagates, so the customer arrives for the wrong service. Requires a shared grounded fact store and a verification agent on high-cost outcomes.
Travel rebooking after disruption
Travel · Customer service
Workflow. Scheduling agent finds alternates, loyalty agent applies benefits, ground-transport agent handles transfers, policy agent validates fare-class eligibility, comms agent confirms with the traveler.
The win. Multi-constraint resolution in seconds instead of minutes.
Failure mode. Non-deterministic pricing across agents: one agent quotes a fare the policy agent later rejects. The fix is a single pricing-of-record service every agent queries, not individual model reasoning.
Customer-service triage
Support
Workflow. Incoming ticket is classified, enriched with customer context, cross-checked against known incidents, routed to self-serve or human with full notes.
The win. Dramatic reduction in first-response time for the 60-70% of tickets that are routine.
Failure mode. Low-confidence tickets incorrectly routed to self-serve. Requires an explicit confidence threshold and a clear escalation policy, not an unbounded retry loop.
Coding assistant as an agent
Developer productivity
Workflow. A task arrives. Agents read the repo, retrieve relevant symbols, write the code, run tests, interpret failures, iterate until green.
The win. Faithful implementation of small-to-medium tasks without hand-holding.
Failure mode. Agents "make the tests pass" by weakening assertions. Requires guardrails that forbid edits to test files and a final human review for behavioral changes.
Sales-research enrichment
Revenue ops
Workflow. An agent takes a lead, pulls firmographics, identifies decision-makers, drafts personalized outreach, queues it for human approval.
The win. Research quality that used to take an SDR 20 minutes, delivered in seconds.
Failure mode. Fabricated job titles or confused namesakes. Requires grounding against a verified person-data provider and a minimum-confidence gate on enrichment facts.
Procurement and supplier vetting
Operations
Workflow. Agents summarize supplier RFPs, cross-reference compliance requirements, run risk checks against sanction lists, prepare a recommendation.
The win. Consistent, auditable summaries across hundreds of supplier submissions.
Failure mode. Hallucinated compliance claims. Requires deterministic retrieval from the actual compliance documents and a policy agent that blocks any unsupported claim.
SRE triage on incident alerts
Reliability
Workflow. Alert fires. Agents pull recent deploys, correlate with traces, check dependency status, generate a hypothesis and runbook.
The win. Time-to-initial-hypothesis collapses from 10-20 minutes to under a minute.
Failure mode. Confident but wrong hypothesis anchors the on-call engineer. Requires presenting hypotheses as candidates with evidence, not conclusions.
Contract review and redlining
Legal ops
Workflow. Agents compare incoming contracts against the standard playbook, flag deviations, suggest redlines, summarize risk for the reviewing lawyer.
The win. Reviewers focus on the 15% that matters instead of reading the full document.
Failure mode. Missed non-standard risk the playbook does not cover. Requires the agent to output what it did not evaluate, so the lawyer knows the boundaries of the review.
Financial reconciliation
Finance
Workflow. Agents match transactions across systems, investigate discrepancies, draft journal entries, flag exceptions for human approval.
The win. Month-end close shortens by days when discrepancies are resolved by agents before the team sees them.
Failure mode. Mis-reconciled edge cases with false confidence. Requires strict amount thresholds above which the agent cannot act alone and a mandatory audit trail.
Marketing-asset production
Content
Workflow. Agents research a topic, draft copy, generate assets, localize, prepare a multi-channel package, queue for editorial review.
The win. Campaign-speed content pipeline without a proportional headcount increase.
Failure mode. Generic output that passes the eye test but underperforms. Requires brand-voice grounding against existing high-performing assets and a performance feedback loop.
Patterns across all ten
Two patterns recur in every example. First, the failure is almost never the model. It is the coordination, grounding, or policy layer around the model. Second, the fix is almost never a better model. It is stronger guardrails, deterministic retrieval, and verifiable tracing. A hard lesson for teams expecting the next model release to do the work that architecture has to do.
The related deep-dive on hallucinations at system scale explains why compounding errors beat per-call accuracy every time. And for the operational lens, see AI SRE: the converging operating model.
Frequently asked questions
What makes a workflow a good candidate for an agentic system?
Three markers: high volume (so the instrumentation overhead pays off), clear goal (so success is definable), and bounded tool set (so guardrails are enforceable). If any of the three is missing, a simpler automation usually beats an agentic one.
What usually fails first in agentic deployments?
The coordination layer, not the model. The most common incidents come from agents disagreeing on the same fact, retrying silently into cost spikes, or executing on stale context. Grounding and observability catch these before the model ever becomes the bottleneck.
How many agents is too many?
The useful ceiling is determined by how deep your traces go, not how many agents you can instantiate. If you cannot reconstruct end-to-end what happened in a single request, you already have too many. Cap expansion until the tracing and cost-attribution story catches up.