Overview
trycompai/crm treats an AI agent as ongoing work inside a system of record, not a chat box beside a database. The series follows how API events become durable tasks, how CRM context and evidence govern facts, how leases and recovery preserve work, and how egress, scope, typed actions, and approval constrain automation. Its central boundary is explicit: these mechanisms make uncertainty inspectable and recoverable, but do not prove source freshness, production accuracy, uptime, or scale.
Learning outcomes
- trycompai/crm writes facts only through evidence-aware paths; weaker support becomes a proposal, contradiction is held, and insufficient support can remain absent.
- The API records events while durable tasks preserve intent; the agent owns interpretation, and leases, retries, and outcomes make failure visible.
- Internal CRM history is read before paid research, while external queries, sandbox access, and team-agent actions remain explicitly bounded.
- The repository demonstrates mechanisms and tests, not production accuracy, source freshness, uptime, adoption, or scale.
Keywords
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PlansFAQ
How does trycompai/crm prevent AI guesses from becoming CRM facts?
trycompai/crm requires evidence entries tied to observations, applies them through a controlled write path, and can propose, hold, or leave a field empty instead of storing an unsupported claim.
How does trycompai/crm keep agent work recoverable?
trycompai/crm stores events as durable tasks with due times, leases, attempt counts, outcomes, and retirement states, so work can be reclaimed or reconciled after slow providers, restarts, or competing workers.
What can an agent do in trycompai/crm without unrestricted access?
In trycompai/crm, the agent can read needed internal context, but external questions, sandbox use, record scope, and typed CRM or Slack actions are constrained by capability and approval checks.
Does trycompai/crm prove that its automation is accurate in production?
No. The repository supports design intent, implementation mechanisms, and selected tests, but it does not prove production accuracy, provider freshness, uptime, adoption, or scale.