What is AI Agents?

Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. The shift represents a fundamental change in how software interacts with business processes: instead of humans operating tools, agents operate tools on behalf of humans.

In sales, the agent model addresses the capacity constraint that limits every team. A human SDR can research 10-15 prospects per day and send 50-80 personalized emails. An AI agent can research 500 prospects and generate 500 personalized messages in the same time — not because it is faster at each task, but because it operates continuously without breaks, meetings, or context-switching.

According to McKinsey, AI agents could automate 30% of current sales tasks by 2027, with the highest impact on research, data entry, initial outreach, and reporting. However, the technology is early: current agents excel at structured, repeatable tasks but struggle with nuanced negotiation, creative problem-solving, and relationship building. The most effective implementations position agents as tireless assistants that handle high-volume, low-judgment tasks while humans focus on high-judgment, high-value interactions.

AI agents operate through a perceive-reason-act loop, often described as the "agent architecture."

**Perception** is how agents gather information about their environment. In sales, this means ingesting data from CRMs, email platforms, signal databases, calendar systems, and web sources. A prospecting agent perceives new signals (funding rounds, job changes, technology installs) and new data (updated CRM records, engagement events).

**Reasoning** is the decision-making step where agents evaluate options and plan actions. Powered by LLMs (like GPT-4, Claude, or Gemini), agents reason about which prospects to prioritize, what message angle to use, whether to escalate an issue, and how to sequence their actions. Advanced agents use chain-of-thought reasoning, tool selection (choosing which APIs to call), and memory (retaining context from previous interactions).

**Action** is the execution of decisions. Agents interact with external systems through API calls: sending emails via email platforms, updating records in CRMs, scheduling meetings via calendar APIs, and triggering workflows in automation tools. The key distinction from simple automation is that actions are chosen dynamically by the agent, not predetermined by a static workflow.

**Tool use** gives agents capabilities beyond their core LLM. An agent might use a search tool to research a company, an enrichment API to fill missing data, a calculator to estimate deal value, and an email tool to send a message — selecting and sequencing these tools based on the task at hand.

**Memory and learning** enable agents to improve over time. Short-term memory retains context within a conversation or workflow. Long-term memory stores preferences, successful strategies, and institutional knowledge. Some agent architectures incorporate feedback loops where human corrections are used to refine future agent behavior.

**Multi-agent systems** coordinate multiple specialized agents. A research agent gathers prospect intelligence, a writing agent drafts messages, a quality agent reviews for accuracy, and a delivery agent handles sending. This specialization mirrors how human teams organize work.

Autobound provides the intelligence layer that powers AI agents in B2B sales. Whether you are building your own agentic workflows or using an existing AI SDR platform, Autobound's Generate Insights API delivers the signal intelligence, prospect research, and personalized messaging that agents need to operate effectively. The API is designed for programmatic consumption — exactly what agents require to perceive, reason about, and act on prospect data.