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A team treats Breeze agents and HubSpot workflows as separate worlds — agents do their thing in a dashboard, workflows do automation, and a rep stitches the two together by hand. The Run Agent workflow action collapses that gap. It is in private beta, and the configuration patterns that work are the ones that treat the agent like any other workflow step that can fail.

What the action does

The workflow library gains a new action called Run Agent. You pick which agent to invoke, pass context properties, configure timeout, and capture structured outputs that downstream steps reference like any other property.

Action: Run Agent
  Agent:        Company Research Agent
  Inputs:       contact.email, contact.companyname
  Outputs:      industry, employee_count, funding_stage, ai_confidence
  Timeout:      60 seconds
  On error:     skip downstream branch (set agent_skipped = true)

The output is structured. Downstream steps branch on output values rather than parsing free text.

Configuration patterns

Property mapping:
  Pass only what the agent needs (smaller context = faster + cheaper)
  Bind outputs to typed properties (text, number, dropdown)
  Default values for nullable outputs
  Confidence score as a separate output for branching

Error handling:
  Timeout: skip and flag for human review
  Low confidence (<0.6): route to manual queue
  API failure: retry once, then fallback workflow
  Rate limit: defer 60 seconds, retry once

A workflow without explicit error handling on agent failure stalls silently. Always define the fallback.

Pattern: enrich before route

A common high-value use:

Workflow: Inbound demo request routing
  Trigger: Form submission "Request demo"
  Step 1:  Run Agent: Company Research
           Inputs: contact.email, contact.companyname
           Outputs: industry, employees, funding, confidence
  Step 2:  If confidence < 0.5: route to SDR for manual research
  Step 3:  Branch on industry + employees:
           Healthcare AND >500   -> Sarah (Healthcare AE)
           Software AND >1000    -> Tom (Enterprise AE)
           Else                  -> SDR queue
  Step 4:  Notify owner with enrichment summary in Slack

Routing accuracy improves measurably and the manual research step disappears.

Pattern: triage before escalate

Workflow: Support ticket triage
  Trigger: Ticket created on tier-1 pipeline
  Step 1:  Run Agent: Customer Agent
           Inputs: ticket.subject, ticket.body, contact.history
           Outputs: status, intent, summary, suggested_action
  Step 2:  Branch on status:
           "resolved"        -> close ticket, send CSAT
           "needs_human"     -> assign to tier-1 queue with summary
           "needs_eng"       -> escalate to engineering queue

The human picks up tickets the agent could not handle, with the agent’s summary attached so they do not start from scratch.

Output binding gotchas

- Output names are case-sensitive
- Renaming an output silently breaks downstream references
- Untyped outputs fall back to text (not great for branching)
- Null handling: define what happens when output is absent
- Long string outputs may truncate in property storage

Document output schemas as part of the workflow design. Treat them like API contracts.

Cost monitoring

Outcome-based pricing on agents means each invocation has a unit cost. A workflow that calls an agent on every record can run up significant spend:

Daily metrics per workflow:
  - Agent invocations
  - Average tokens per invocation
  - Cost per invocation
  - Skipped due to low confidence
  - Failed due to error

Pre-filter agent calls when possible. A workflow that runs Company Research only on contacts missing industry data costs a fraction of one that runs it on every contact.

Private beta caveats

Private beta means contracts can change. Avoid building load-bearing automation on Run Agent until it goes GA. Use it for non-critical enrichment, triage, and personalization where a temporary outage is recoverable. Document fallback paths so a beta-feature regression does not cascade into customer-facing failure.

Migration plan when GA arrives

- Audit workflows still using Run Agent
- Verify configuration matches GA action signature
- Re-test in sandbox with GA version
- Update fallback paths if error contracts changed
- Promote to production with monitoring

What to do this week

Pick one manual research or triage step in a high-volume workflow, prototype the Run Agent equivalent in test mode, define explicit error handling for timeout and low confidence, and instrument cost plus confidence metrics before requesting wider beta access.

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