By Erol Karabeg,
Austin Duncan, Rihad Seherac, and Bojan Imamovic
September 26, 2025
CIOs and CTOs are under pressure to do more with less. The fastest path we see right now is to “hire” AI agents as digital coworkers who answer questions, make decisions, and complete tasks across systems. Lately, our teams start new initiatives not with a classic journey map or a monster backlog, but with a job description for an AI agent. Define the role, the remit, and the guardrails, then let the technology slot into the work. It is pragmatic, fast, and measurable.
AI agents have moved beyond chatbots. They understand natural language, connect to your CRM, ERP, HRIS, and data stores, and then act across them. Think of them as junior colleagues embedded in the flow of work who retrieve knowledge, orchestrate workflows, and improve with feedback. The result is less swivelchair effort and more outcomes per person.
PlainEnglish:
RAG = answers.
Retrieval-augmented generation is your knowledge assistant that finds, summarizes, and explains.
Agents = actions.
Agentic AI plans the steps, calls tools and APIs, and closes the loop under your policies.
Together they form one continuum: insight to execution.
Leaders care about throughput, reliability, and spend. Agents attack the waste hiding in search, handoffs, and manual data work. We consistently see knowledge workers reclaim meaningful hours weekly when a search or assistant agent unifies content and answers questions where people already work. Pair that with integration and support agents, and you get fewer errors, faster cycles, and tighter governance. Early adopters report sizable productivity gains and better total cost of ownership when agents handle repeatable tasks and humans focus on exceptions.
Before tooling, write the agent job description (AJD). Treat the agent as a teammate.
We now use this AJD to align stakeholders, pick a first use case, and determine the smallest viable production scope.
1.) Enterprise search and employee productivity
Problem: People lose hours hunting across drives, wikis, tickets, and email.
Agent outcome: A single “ask” surface that pulls the right answer with citations from all your sources, inside Slack or Teams, and tuned to permissions. Onboarding accelerates, repetitive questions fall, and decisions improve with context.
Problem: Human middleware glues systems together with exports, spreadsheets, and copy-paste.
Agent outcome: A digital ops specialist that extracts data from emails and documents, transforms and validates it, then updates downstream systems automatically. It adapts to field or schema changes and flags anomalies for humans.
Problem: Ticket backlogs, inconsistent responses, and slow triage.
Agent outcome: Instant ticket summaries, suggested replies, autonomous resolution for common issues, and clean escalation with full context. Net effect: faster first response and happier teams.
In one financial SaaS product, an agentic research assistant automated data gathering and validation, cutting cycle time by 75 percent and freeing analysts for higher-value work. That is the pattern: automate the grunt work, keep humans in control.
(Story: “Transforming Data Intelligence with AI Automation.”)
Agents shine when the foundation is sound. In mortgage and title workflows, migrating to Encompass Partner Connect and modernizing supporting services reduced manual steps and cut fee-quote time by over 50 percent in one program. That kind of simplification makes it easier for agents to act end-to-end across systems.
Modern cloud-native platforms also improve reliability and operating cost, which raises the ceiling for safer, scalable automation.
Step 1: Inventory and score use cases.
In 3 weeks, identify your top 3 to 5 agent opportunities by time saved, error cost, and risk. If you want a structured pass, our Agentic AI Assessment service aligns sponsors and quantifies ROI to de-risk investment.
Stand up one agent with a narrow mandate and strong guardrails, treat it like a new hire in probation, and put it into production early with a limited scope so you can watch it perform. Measure everything and keep humans in the loop for final approvals. Use our Agentic AI Innovation service to prove value with a prototype in as little as 4 weeks.
Clone the pattern into support, ops, or finance. Standardize prompts, integrations, and evaluation. Watch adoption curves and error rates.
As wins compound, harden security, monitoring, and change control. Move from “hero agents” to a catalog with versioning, runbooks, and metrics. When you are ready to scale delivery, our Agentic AI Production service focuses on getting reliable agents into the flow of work.
Successful programs balance speed with control.
Here is the thing. Agents are not here to replace your teams. They are here to elevate them by removing low-value steps and compressing time. Start with one AJD. Prove the outcome. Then scale what works.
If you want a practical next step, let’s talk about a workflow that slows your team down. We will co-draft the agent’s job description, identify the guardrails, and outline a production-ready thin-slice. If it makes sense, explore a quick assessment or a small build to validate impact in your environment.
Let’s connect to talk about the job description for your AI agent. We’ll define the role, the remit and the guardrails.
Thoughts, breakthroughs, and stories from the people building what’s next.
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