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Which tasks can software execute reliably?

Agentic operations

Agentic ABM uses software agents to execute bounded account tasks, retrieve evidence, propose actions and record workflow state. A reliable operating model defines tool permissions, acceptance checks, retry behaviour and the decisions that require accountable approval.

The work you should leave with

A versioned workflow with traceable inputs, acceptance criteria and exception handling.

Define your account context and baseline before choosing a method. Record the evidence, unknowns, accountable owner and review decision. The guides below address different parts of that task.

Inspect a sample account play

Define the task contract before the agent

Specify the input fields, permitted sources, tools, output format and acceptance threshold for one bounded task. Account research, message drafting and CRM write-back need different permissions and checks. Decide what happens when evidence is missing or contradictory. The workflow must be able to return an unresolved state rather than fill a required field with a confident guess.

Treat retrieved pages as evidence to inspect. Page text does not grant an agent permission to change records, contact an account or disclose data. Keep instructions and external content separate, and restrict tool access to the actions required by the current task.

Evaluate the workflow on representative work

Use a small set of representative account tasks with an agreed quality rubric. Check source support, account matching, required fields and the reviewer’s acceptance decision. Keep failures and corrections in the comparison. A fast draft that requires substantial rework may be less useful than a slower draft that meets the acceptance criteria.

Version the prompt, model settings, source set and acceptance rules together. When a component changes, rerun the affected tasks before extending its use. The aim is to establish which work the system can execute reliably in your environment, with the review effort included.

Make exceptions visible to an owner

A fictional research agent finds two different headcount figures for an account. It records the dates and source types and requests review instead of selecting the larger value. A message agent that depends on that field pauses the affected draft. This preserves the dependency between evidence quality and the downstream message.

Record the failure, assigned owner, correction, retry decision and final status. Bound retries and make external actions reviewable. Inspect accepted outputs per hour and cost per accepted output alongside failure rates; tool-call volume and generated word count are activity measures, not proof of useful account work.

Start with these decisions.

Evidence to inspect

These sources inform a test design. They do not establish an Outsell result or guarantee that an effect transfers to your account programme.

ReAct: Synergizing Reasoning and Acting in Language Models — Design observable tool steps with bounded actions and stop conditions.

Lost in the Middle: How Language Models Use Long Contexts — Move critical constraints around a representative brief and test recall.

When combinations of humans and AI are useful — Compare human, agent and combined versions of the same task.

Generative Artificial Intelligence Profile: NIST AI 600-1 — Connect material failure modes to owners, checks and monitored evidence.

Complete the operating picture.