Agentic AI services: agents that plan, act and check their work

Agentic AI is AI that works toward a goal instead of answering a single prompt. An agentic system plans the steps, uses tools such as APIs, databases and business applications to carry them out, checks the results and asks a person to approve decisions that matter. It is usually built from one or more AI agents running on large language models.

Generative AI creates content, such as text, summaries, answers or code, when it is asked. Agentic AI uses generative models as its reasoning engine and adds planning, tool use and action toward an outcome. A model that drafts a follow-up email is generative AI. A system that reads the deal history, drafts the email, updates the CRM and asks for approval before sending is agentic AI.

Not quite. An AI agent is one piece of software that carries out tasks with some autonomy. Agentic AI describes the wider approach, where one or more agents pursue a goal with planning, tools, memory, guardrails and evaluation. Production systems often have several agents, such as a supervisor that delegates to specialists.

Each agent gets least-privilege access to the tools it needs, guardrails on what it can say and do, and an approval step before actions that change records or reach customers. In the iSEEit build, analysis runs automatically while write-back to the CRM needs human approval or a tenant-configured confidence threshold, and 100% of autonomous actions were logged with source citations and a complete approval trail.

We measure an agent before and after release. Before release, a golden-dataset evaluation scores its outputs before any prompt, model or tool change is promoted to production. After release, we track the business result the agent was built for. At iSEEit, agent MEDDPICC assessments reached 92% agreement with expert human review on the evaluation dataset, and opportunities with complete MEDDPICC data rose from 42% to 85% within 6 months.

We build on Amazon Bedrock, OpenAI and Anthropic Claude, with models such as Claude, GPT-4, LLaMA, Mistral and Cohere and frameworks such as LangChain, AutoGen and CrewAI. Inside Salesforce we build with Agentforce. iSEEit's agents run on Amazon Bedrock with Anthropic Claude models.

Model routing, prompt caching and token budgets send each task to the smallest model that can do it, and token-budget ceilings per agent run cap what a single run can spend. At iSEEit this cut inference cost per processed deal by 32%.

Pick one workflow that repeats every day and has a clear owner. HabileLabs runs a free AI readiness assessment so you can plan before you commit to a build. On Anthropic Claude we also offer a 14-day pilot that starts with one practical workflow and a clear production path, and Agentforce deployments covering two or three use cases typically take four to eight weeks.