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AI Agents & Agentic Workflows

Autonomous agents that plan, call your tools and finish multi-step work — with guardrails.

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What is an AI agent?

An AI agent is software that uses a large language model to plan a task, call tools and APIs, check its own results and keep going until the job is done. Unicorn Softech designs, builds and operates AI agents that connect to your CRM, ERP, databases and internal APIs, with human approval steps, permissions and full tracing.

Inside an agent

What an AI agent is made of

PART 1 OF 7

Model

The language model that reads the task and decides the next step.

In your project: We pick it per step: a strong model for decisions, a cheaper one for routine work.

See the seven agents we build Design your own agent

See it

What it looks like

How it works

AI Agents & Agentic Workflows in 5 steps

  1. 01Request
  2. 02Plan
  3. 03Call tools
  4. 04Check result
  5. 05Task done

What we deliver

What you get with AI Agents & Agentic Workflows

01

Agent & workflow design

Task decomposition, single vs multi-agent architecture, and the right model for each step.

02

Tool & function calling

Typed tools over your APIs, databases and SaaS apps, including MCP servers.

03

Memory & state

Short-term context, long-term memory and resumable runs for long tasks.

04

Guardrails & approvals

Least-privilege credentials, policy checks and human-in-the-loop for risky actions.

05

Evaluation

Test suites that score agent runs before every release, not after an incident.

06

Observability

Step-level traces, cost and latency dashboards, and replay for debugging.

Common use cases

  • Customer support agents that resolve tickets end to end
  • Sales agents that research leads and update the CRM
  • Back-office agents for invoices, reconciliation and reporting
  • DevOps agents that triage alerts and run runbooks
  • Research agents that read documents and write summaries

Tools and technology

We choose per project. These are the tools we use most for ai agents & agentic workflows.

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • LangGraph
  • Model Context Protocol
  • Python
  • Node.js
  • PostgreSQL
  • Agent & workflow design
  • Tool & function calling
  • Memory & state
  • Guardrails & approvals
  • Evaluation
  • Observability
  • Customer support agents that resolve tickets end to end
  • Sales agents that research leads and update the CRM
  • Back-office agents for invoices, reconciliation and reporting
  • DevOps agents that triage alerts and run runbooks
  • Research agents that read documents and write summaries

FAQ

AI Agents & Agentic Workflows: common questions

What is the difference between a chatbot and an AI agent?

A chatbot answers questions. An AI agent takes actions: it calls tools, updates systems and completes multi-step tasks, checking its own work along the way.

How do you keep AI agents safe?

Agents run with least-privilege credentials, every tool call is validated against policy, risky actions need human approval, and every step is logged so it can be audited and replayed.

How long does it take to build an AI agent?

A focused proof of concept for one workflow typically takes a few weeks. Production rollout depends on how many systems the agent must integrate with and the approval rules you need.

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Ask questions across all your documents and get cited, permission-aware answers.

Let's talk about AI Agents & Agentic Workflows

Book a free 60-minute consultation. You'll leave with a feature list, a rough estimate and a practical first step — no obligation.

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