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AI Agent Development

AI Agent Development Services

Build AI agents that can plan multi-step work, call approved tools, retrieve trusted context, follow business rules, ask for human approval, and leave an observable trail of decisions and actions.

Discuss AI agentsSee how we work

Trusted by product, analytics and growth teams

ObinoNextGrowth LabsFisdomZoomob

  • Renewal quotes

    Reads the account and seat usage, drafts the quote, and waits for a sales lead to approve it.

    1. Read the Northwind account
    2. Seats used: 140, now 162
    3. Drafted the quote for 162 seats
    4. Sent to the sales lead to approve

    6 hsaved each week

  • Refund triage

    Checks the order against policy, settles small refunds, and hands the rest to a person.

    1. Matched order 4471
    2. Inside the 30-day policy
    3. Small refund settled
    4. Larger claims go to a person

    3 minaverage handling time

  • Weekly reports

    Pulls the numbers from the warehouse and writes the summary your team reviews on Monday.

    1. Queried the warehouse
    2. Compared with last week
    3. Wrote the summary
    4. Posted for Monday review

    41 sper report

  • Lead research

    Enriches new leads from the CRM and public sources, then scores and routes them.

    1. New lead from the CRM
    2. Found company size and stack
    3. Scored 82 of 100
    4. Routed to the right rep

    11 minsaved per lead

Illustrative examples and figures

  1. 01 ChooseChoose the workflow where an agent can safely create leverage instead of adding model risk.
  2. 02 ConnectConnect tools, APIs, data, retrieval, permissions, and approval checkpoints around the agent loop.
  3. 03 MeasureMeasure reliability with eval suites, trace logs, tool-call review, fallback paths, and cost controls.

Scope

Agent systems with guardrails

Production-ready teams frame AI agents as production systems with tool use, permissions, evals, observability, and rollback, not one-off prompts.

  1. Workflow agents

    Automate research, triage, routing, summarization, enrichment, and follow-up steps.

What You Get

What your AI agent engagement includes

We design agents as controlled production systems: bounded workflows, approved tools, evaluations, and human review where it matters.

  • agent_workflow.md

    Agent workflow design

    Inputs, goals, planning steps, tools, permissions, failure paths, and approval gates defined up front.

    trigger    renewal in 14 days
goal       quote ready for approval
done when  approved and sent

  • tools.yaml

    Tool and API layer

    Approved actions connected through clear interfaces so the agent can act without brittle shortcuts.

    crm.get_accountreadauto
    billing.draft_quotewritedraft
    email.sendwriteapproval

  • evals/

    Evaluation suite

    Scenario tests, edge cases, expected outcomes, and regression checks used to improve reliability.

  • controls.md

    Operational controls

    Logging, rate limits, cost tracking, rollback behavior, and human-in-the-loop checkpoints.

    max steps  12
max cost   ₹25 per run
on error   stop, tell the owner

Process

Design the agent around one valuable job

We move from a narrow workflow to a tested system rather than chasing broad autonomy too early.

  1. Step 01 of 05

    Workflow selection

    Choose a process with clear inputs, decisions, outputs, and review requirements.

  2. Step 02 of 05

    Agent architecture

    Design context, tools, prompts, memory, retrieval, approvals, and fallback behavior.

  3. Step 03 of 05

    Prototype

    Build a working agent slice and test it against realistic examples.

  4. Step 04 of 05

    Hardening

    Add evaluations, permissions, rate limits, logging, error handling, and human review.

  5. Step 05 of 05

    Operational rollout

    Launch with monitoring, feedback loops, and iteration against measured reliability.

Best Fit

For repeatable knowledge work with tools

Automate routing, summaries, checks, and repetitive coordination steps.

One run log. 3 of 7 columns matter to Operations teams; every action can be traced.

AgentToolsApprovalEval passRuns / wkCost / runOwnerData used
Renewal agentCRM, billingBefore sending90%120₹3.10Sales opsAccounts, usage
Refund agentOrders, paymentsEvery refund84%340₹1.40SupportOrders
Report agentWarehouseNone88%60₹6.20Data teamMarts
Triage agentHelpdeskNone91%900₹0.40OpsTickets

Illustrative rows

Proof

Why partner with Multivariate

We approach agent work as product engineering: bounded use cases, measurable quality, integrations, and controls before scale. Each engagement is shaped around practical buying criteria: integrations, governance, quality checks, handoff, and measurable operating outcomes.

  1. Exhibit A

    We keep agent actions observable.

  2. Exhibit B

    We build human review into risky steps.

  3. Exhibit C

    We design for maintenance as models and tools change.

FAQ

AI agent development FAQs

Multivariate analytics team

Replies within 24 hours

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

A chatbot mainly responds in conversation. An agent can also use tools, follow workflows, make intermediate decisions, and complete tasks with guardrails.

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