Agentic GTM & RevOps glossary

The foundational terms that define the category and operating model.

• Agentic GTM — a go-to-market operating model where humans and AI agents work together across sales, marketing, CS, partners, systems, and data to execute GTM work with more intelligence, governance, and control.
• AI-native GTM — a GTM organization designed around agents from the ground up, rather than one that bolts AI onto legacy human-only workflows. Not AI-enabled. AI-native.
• Agentic operating layer — the infrastructure that connects CRM, GTM data, models, agents, workflows, governance, and human review into one system; the layer your GTM organization works through.
• Agentic operating model — the way an organization structures work so agents execute the repeatable tasks and humans manage the last mile.
• System of record vs. system of work — your CRM remains the system of record; the agentic operating layer becomes the agentic system of work that runs on top of it.

How agents are defined, coordinated, and run.

• GTM agent — a specialized AI worker that performs a defined go-to-market task (e.g., pipeline inspection, CRM hygiene, lead triage) under scoped permissions and human review.
• Agent orchestration — routing work to the right agent with the right context and sequencing multi-step agentic workflows across GTM.
• Agent runtime — the managed environment that runs agents reliably with queues, retries, rate limits, error handling, and workflow state.
• Tool use — an agent taking real actions in approved systems (CRM actions, lookups, integrations) within scoped permissions and human approval.
• Workflow state — the maintained record of where a piece of agentic work is across steps, handoffs, and approvals, so nothing is lost between agent and human.

What agents reason over, and how model selection works.

• GTM context layer — CRM data plus business context (sales process, ICP, offering, policies) organized into the structured context agents need to do real work.
• Model routing — sending each task to the best-fit model, provider-agnostic, with fallbacks and enterprise controls.
• LLM-agnostic — an operating model that is not locked to a single model provider, using the right model for each task.
• Context assembly — gathering the relevant data and business context an agent needs before it reasons or acts.

The controls that make agentic work safe and attributable.

• Human-in-the-loop — routing agent output to a person to approve, edit, reject, or escalate before anything reaches a customer or the CRM.
• Last-mile management — the human responsibility for judgment, approval, and final decisions after agents prepare the work.
• Permissioning — scoped agent access based on user, role, policy, and workflow; agents need permissions like workers, not freedom like chatbots.
• Data controls — restricting agent access to approved objects, fields, and records, with PII and retention boundaries.
• Audit trail — a record of who initiated, reviewed, approved, and changed what, plus the data used and the model that ran.
• Observability — visibility into agent activity, model usage, workflow performance, and business impact across GTM.
• Agent evaluation — measuring agent quality, accuracy, feedback, rejection rate, and business impact to catch regressions and tune output.
• Shadow AI — ungoverned, unobservable AI activity spreading tool-by-tool across a GTM stack; the risk agentic governance is designed to prevent.

Who owns the operating model and how organizations mature.

• RevOps as architect — RevOps is the champion and architect of the agentic operating model; the entire GTM organization is the surface area that works through it.
• Agentic GTM maturity model — the stages of adoption from manual, human-only GTM to a governed, AI-native operating model at scale.
• Pilot — a focused program that proves agentic GTM on real workflows before broader rollout.
• Rollout — expanding agentic GTM by GTM group, region, or workflow with governance and enablement at each stage.

How consumption-based pricing for agentic GTM works.

• Credit — priced at $1, a bundle of agentic runtime consumed as agents work; not a seat and not a single prompt.
• Consumption pricing — paying for the agentic work performed rather than per seat, so cost aligns to usage.
• Usage sizing — estimating expected credit consumption from your agents, workflows, data volume, model needs, and frequency.
• Business value per credit — the outcome delivered relative to consumption; RevTech optimizes for value per credit, not credit burn.