2 ready-to-build workflows

AI agent workflows for Colleges & universities

Automate application-status Q&A, financial-aid guidance and student-services helpdesk — eligibility and money calls stay human.

Each recipe below is expressed only in VegaDūta's real workflow building blocks — Trigger, Agent, Knowledge, Condition, Approval, Output, Tool (MCP), Voice, Device, Loop, Code and Parallel — so it maps 1:1 to something you can assemble in the Workflow designer. Consequential actions always pass a human Approval step.

1. Application status Q&A and scholarship guidance

Applicants flood the admissions office asking where their application stands and whether they qualify for a scholarship, and staff look each one up by hand and repeat the same eligibility rules dozens of times a day.

Trigger: WhatsApp / Webhook (applicant enquiry)

How the workflow runs

  1. Trigger

    Applicant asks. A WhatsApp or Webhook trigger starts on any status or scholarship question, in the applicant's language.

  2. Agent

    Identify the applicant. An Agent node verifies identity and the application reference before anything is looked up.

  3. Tool (MCP)

    Fetch live application status. A Tool (MCP) node reads the current stage from your SIS/admissions system. There's no first-party connector — the status is fetched via MCP.

  4. Knowledge

    Explain scholarship criteria. A Knowledge node answers financial-aid and scholarship questions strictly from the official criteria documents — grounded, with citations.

  5. Approval

    Eligibility rulings go to an officer. An Approval node routes any actual eligibility or award decision to a financial-aid officer — the agent explains the rules, a human decides who qualifies.

  6. Output

    Reply + record. An Output node sends the status and grounded guidance on WhatsApp and logs the interaction.

Channels & connectors

  • WhatsApp
  • Webhook
  • Sarvam (22+ Indian languages)
  • Knowledge base
  • Approval / HITL
  • Tool (MCP → SIS/admissions)

Outcome

Applicants get their real status and accurate scholarship guidance instantly, while any eligibility or award decision stays with a human officer.

Why it helps

Removes the manual per-applicant lookup and the hundredth repeat of the same criteria, while keeping every money/eligibility call with a person — the agent informs, it never rules.

Build spec

1 agent1 applicant-facing agent; the status lookup, the eligibility Approval and the reply/log are workflow nodes.

System prompt (paste-ready)

You are an admissions assistant for {{institution_name}}. Verify the applicant's identity and application reference before looking anything up. Report the application status the system returns, and answer scholarship and financial-aid questions STRICTLY from the official criteria documents in Knowledge, with citations. You EXPLAIN the criteria — you do NOT decide eligibility, confirm an award, or tell an applicant they qualify; any actual eligibility or award decision is a financial-aid officer's at the approval step. Reply in the applicant's language and never reveal another applicant's data.

MCP connectors

  • SIS / admissions system — via a Tool (MCP) node (no first-party connector)
  • WhatsApp — via the messaging channel
  • Sarvam — 22+ Indian languages

Built-in tools

  • knowledge_search (scholarship + financial-aid criteria)

Guardrails

  • Never rules on eligibility or confirms an award — it explains the criteria; the financial-aid officer decides who qualifies at the Approval node
  • Identity + application reference are verified before any status lookup; an applicant only ever sees their own data
  • Scholarship guidance is grounded in the official criteria with a citation, never an assumed or invented figure

Output & delivery

The agent verifies identity → a Tool node reads the live application stage from the SIS via MCP → a Knowledge node grounds scholarship guidance → any eligibility/award call goes to the financial-aid officer at the Approval node → an Output node sends the status and guidance on WhatsApp and logs the interaction.

2. Student-services helpdesk (hostel, exams, transcripts)

Students queue at counters and flood inboxes over hostel, exam and transcript queries, and staff answer the same routine questions by hand while genuine exceptions wait behind them.

Trigger: WhatsApp / Webhook (student query)

How the workflow runs

  1. Trigger

    Student query comes in. A WhatsApp or Webhook trigger catches the request, in the student's language.

  2. Knowledge

    Answer from the handbook. A Knowledge node grounds routine answers (hostel rules, exam schedule, transcript process, deadlines) in the university's own handbook and circulars — with citations.

  3. Agent

    Handle or raise the request. An Agent node resolves routine questions directly and, for actionable requests like a transcript, structures the request cleanly.

  4. Condition

    Separate routine from exceptions. A Condition node splits self-serve answers from anything needing staff judgement (fee disputes, special cases).

  5. Tool (MCP)

    Raise the request in the system. A Tool (MCP) node files a transcript or hostel request in your student system for processing.

  6. Output

    Confirm + track. An Output node confirms what's happening and the expected timeline on WhatsApp.

Channels & connectors

  • WhatsApp
  • Webhook
  • Sarvam (multilingual)
  • Knowledge base
  • Tool (MCP → student system)

Outcome

Routine hostel/exam/transcript questions answer themselves from the handbook and actionable requests get logged, so staff spend their time on real exceptions.

Why it helps

Deflects the high-volume, repetitive counter and inbox questions with grounded answers, while genuine exceptions reach a human faster because the routine load is gone.

Build spec

1 agent1 helpdesk agent; the routine/exception split and the request-raising Tool call are workflow nodes.

System prompt (paste-ready)

You are a student-services helpdesk assistant for {{institution_name}}. Answer routine hostel, exam, transcript and deadline questions STRICTLY from the university's handbook and circulars in Knowledge, with citations. For an actionable request like a transcript or hostel change, structure it cleanly as {student, request_type, details} for the system. Anything needing staff judgement — a fee dispute, a special case, an exception to policy — you do NOT decide; route it to staff. Reply in the student's language and only ever access the requesting student's own records.

MCP connectors

  • Student information system — via a Tool (MCP) node (no first-party connector)
  • WhatsApp — via the messaging channel
  • Sarvam — multilingual

Built-in tools

  • knowledge_search (student handbook + circulars)

Guardrails

  • Grounded-only: routine answers come from the handbook/circulars with a citation, never a guess
  • Fee disputes, special cases and any policy exception are never decided by the agent — they route to staff judgement
  • A student only ever sees their own records; a raised request is logged and tracked, not actioned beyond filing it

Output & delivery

The agent answers routine questions from the handbook Knowledge → the Condition node separates self-serve answers from exceptions → a Tool node files a transcript/hostel request in the student system via MCP → an Output node confirms what's happening and the expected timeline on WhatsApp.

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