Cruzata
AI & Automation

AI Agent Development: Autonomous Workflows That Take Action

AI agents go beyond answering questions. They plan, call your tools and complete tasks, with the controls you need to trust them.

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Chatbot versus workflow versus agent

What it doesBest used for
Chatbot Answers questions in conversationFAQs, support deflection, guidance
Workflow automation Runs fixed steps when triggeredPredictable, repeatable processes
AI agent Plans steps and chooses tools to reach a goalVariable, multi-step tasks with judgement

Agents designed for control, not just capability

An effective agent combines a language model with tools (APIs, databases, browsers), memory and a defined goal. The risk is giving it too much freedom. We design agents with clear scopes, permissions, approval steps and audit logs.

The result is software that handles research, triage, data entry, reconciliation or outreach while a human stays in charge of consequential decisions.

  • Tool and API integration
  • Human approval checkpoints
  • Memory and context management
  • Tracing, evaluation and cost limits

Agent building blocks

Planner
Breaks goals into steps
Tools
Calls your systems
Memory
Context across tasks
Guardrails
Limits and approvals

Agents in practice

Research accounts, enrich CRM records and draft personalised outreach.

  • Lead research and enrichment
  • CRM updates
  • Meeting preparation
  • Follow-up drafts

Triage tickets, look up orders and propose resolutions.

  • Ticket classification
  • Order and account lookup
  • Refund workflows with approval
  • Knowledge updates

Match invoices, chase documents and prepare reports.

  • Invoice matching
  • Expense checks
  • Reconciliation support
  • Report drafting

Triage alerts, summarise incidents and automate runbooks.

  • Incident summaries
  • Ticket routing
  • Runbook execution
  • Code review assistance

Building a reliable agent

  1. 1

    Define the job

    Goal, boundaries, success criteria and failure modes.

  2. 2

    Connect tools

    Safe, permissioned access to the systems it needs.

  3. 3

    Build & evaluate

    Test on realistic tasks with measurable scoring.

  4. 4

    Add oversight

    Approvals, logging, rate and cost limits.

  5. 5

    Deploy & tune

    Roll out gradually and refine from traces.

Agent technology stack

Agent frameworks

  • LangGraph
  • OpenAI Agents SDK
  • CrewAI
  • AutoGen
  • Model Context Protocol (MCP)
  • Semantic Kernel

Models

  • GPT
  • Claude
  • Gemini
  • Llama
  • Mistral

Integration & ops

  • REST / GraphQL
  • n8n
  • Zapier
  • Temporal
  • Langfuse
  • PostgreSQL
  • Redis
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FAQ

AI Agents: frequently asked questions

An AI agent is software that uses a language model to plan and carry out tasks by calling tools, such as APIs and databases, and adapting based on results, rather than only generating text.

They can be, with the right design: narrow permissions, human approval for important actions, input and output checks, spending limits and complete audit trails.

Tasks that are multi-step, variable and text-heavy, such as research, triage, data gathering and drafting. Fixed, high-volume processes are often better served by classic automation.

Yes. We connect agents to your CRM, ERP, ticketing and databases through secure APIs or the Model Context Protocol.

We build evaluation sets from real tasks and track success rate, error types, latency, cost and human override rates.

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