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AI Agents

AI that doesn't just answer, it completes the task, with you in control.

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Typical timeline6–12 weeks
ControlApprovals and full action logs
ToolingLLMs, your APIs, workflow orchestration
AI Solutions

AI Agents, explained.

An AI Agent is software that uses a language model to plan and carry out multi-step tasks by calling tools: reading records, updating systems, sending messages and checking its own results. It matters because many tasks aren't one question and one answer; they are a chain of lookups, decisions and updates that people currently do by hand across several systems.

Esscentra builds agents around a specific job, gives them access only to the tools and data that job needs, and puts approval checkpoints wherever an action is costly or hard to undo. Every step the agent takes is logged so you can see what it did and why. We test agents against real scenarios, including the awkward ones, before they touch live systems, and start them on a narrow scope that widens as they prove reliable.

The result is work that moves forward without someone shepherding every step. It fits operations teams juggling tasks across several systems, businesses with processes that are too variable for fixed automation, and companies that want AI to act, not just advise.

Who it's for

Built for teams like these.

  • 01

    Operations teams copying information between several systems to finish one task.

  • 02

    Processes with too many variations for fixed, rule-based automation.

  • 03

    Businesses wanting AI to take actions, with a person approving the important ones.

  • 04

    Teams that tried chatbots and now want AI that completes work end to end.

How we work

5 steps, from first call to launch.

  1. 01

    Task mapping

    We map the task step by step: the systems involved, the decisions made and the exceptions.

  2. 02

    Tools & permissions

    We connect the agent to exactly the tools it needs, with the narrowest access that works.

  3. 03

    Agent build & checkpoints

    We build the agent's plan-act-check loop and add approvals before sensitive actions.

  4. 04

    Scenario testing

    We run it against real and edge-case scenarios in a safe environment before going live.

  5. 05

    Supervised launch

    We launch on a narrow scope with close review, then widen it as results hold up.

What you get

Delivered, documented, yours.

  • AI agent running in your environment
  • Tool and API integrations with scoped permissions
  • Approval workflow for sensitive actions
  • Action log and monitoring dashboard
  • Test scenarios and results
  • Runbook for pausing, adjusting and extending the agent
Technologies we use

Our usual stack.

We confirm the final choice with you during scoping.

  • System design
  • OpenAI
  • Claude
  • LangChain
  • Python
  • Node.js
  • PostgreSQL
  • Redis
  • Docker
Questions

Questions about AI agents.

What's the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent completes tasks: it looks things up, makes decisions within rules you set, and updates your systems, often over several steps.
Can the agent do something we didn't approve?
It can only use the tools and permissions we give it, and actions you mark as sensitive wait for a person's approval. Every step is logged for review.
Which systems can an agent work with?
Any system with an API or a reliable integration, such as your CRM, email, spreadsheets, helpdesk or internal databases.
How do you know it works before it goes live?
We test it against real and edge-case scenarios in a safe environment, then launch on a narrow scope with close supervision before widening it.
How much does an AI agent cost?
You get a fixed-price proposal after a free discovery call, based on the task's complexity and the number of systems the agent works with.

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