Agentic AI for project workflows

  • Arrêtez de demander des résumés à l’IA

The problem

A typical day for a Product Manager, ScrumMaster, Delivery Manager or a Business Analyst - context is dispersed:

  • Tickets in YouTrack / Jira
  • Functional notes in Confluence / Youtrack or a team wiki
  • Discussions in chat threads or video calls
  • Engineering trade-offs in GitHub / GitLab PRs
  • The full picture exists only in our heads

Cross-cutting reasoning is manual today. Agentic AI can bridge it.

Level 1 — Pure reasoning

What does a SMART sprint goal look like? Give me 2 examples.

Level 2 — Single tool lookup

List the titles of the user stories closed in our current sprint.

Level 3 — Reasoning + tool

Summarize our last sprint retro notes in Confluence in 5 bullet points, highlighting the improvement actions.

What does a SMART sprint goal look like? Give me 2 examples.

List the titles of the user stories closed in our current sprint.

Level 4 - Reasoning and multiple integrations

Read our last sprint retrospective notes in Confluence. For each improvement action mentioned, find the matching follow-up ticket in YouTrack. List the orphan actions — improvements without a follow-up ticket.

Level 5 - Full end-to-end agentic workflow

Pick up the next 3 tickets in our backlog. Cross-check each against our knowledge base for scope and consistency. Start the development, open a merge request, and post the MR link back into the corresponding YouTrack / Jira ticket.

The challenge of this talk: bringing Level 4 to non-tech roles. Level 5 is the dev frontier - out of scope today.

Recap - 5 levels of prompt complexity

Level Color Capability
1 violet Pure reasoning
2 red One tool lookup
3 violet + red Reasoning + one tool
4 violet + red (multi-source) Reasoning + chained tools
5 + blue + code / write actions

Three solutions tested

Claude.ai — out of the box. Skills + token paste in chat.

LibreChat — self-hosted, open source. Docker, MCP YouTrack, Google SSO.

Custom application — self-hosted, our build. Symfony + tool surface custom (codename ScrumIA).

Claude.ai — out of the box

Pros

  • Zero install, works immediately
  • Top-tier reasoning (Anthropic frontier)
  • Sandbox runs arbitrary API calls → any tool reachable
  • Connector store mature (Atlassian GA Feb 2026, YouTrack MCP, etc.)

Cons

  • 1 license per person at scale (€€€)
  • Token pasted in chat or Skill (UX rough)
  • Permissions blurry — sandbox can read, write, more
  • No enterprise SSO management
  • Skills maintained per user

LibreChat - self-hosted, open source

Pros

  • Free, open source, self-hosted (data sovereignty)
  • Google SSO out of the box
  • Per-user token onboarding with guided UI
  • Any model provider pluggable
  • Clean tool-call UX

Cons

  • MCP-only integration — bound by MCP coverage
  • Some workflows hit a wall (see next slides)
  • Limited UI customization
  • Community maintenance (also a pro)

Custom application - self-hosted, our build

Pros

  • Fine-grained permissions (per user, per op, per role)
  • Full UX control
  • Enterprise SSO + encrypted per-user token storage
  • Any model provider, swappable at runtime
  • Audit log on every tool call
  • Combines MCP and REST → bypasses MCP gaps

Cons

  • Build cost: several weeks for the POC
  • Maintenance on us
  • We own the failure mode

Side-by-side recap

 

Install effort None 1-2 days 1-2 weeks (POC)
Cost €/user/month Hosting only Hosting + dev time
Permissions Loose (sandbox) Standard MCP Fine-grained
Enterprise SSO Limited Yes Yes
Model choice Anthropic only Any Any
Tool coverage Connectors + sandbox MCP only MCP + REST + custom
Audit trail Limited Standard Custom, queryable
Maintenance None (Anthropic) Both Us

MCP is great - but it's not the full API

  • YouTrack MCP → no get_issue_activity. Can't ask "who changed this ticket's priority and when?"
  • Jira MCP (Atlassian official) → no update_issue, no transition_issue, no epic linking, no subtask creation, no delete, no move-to-sprint
  • Confluence MCP → macros, version history, granular space permissions not exposed

Consequence — for any audit, drill-down, or batch workflow, you need REST fallback.

  • LibreChat → wall.
  • Custom application → we wire what's missing.

Why not the SaaS-native AI?

"Jira and YouTrack have their own AI now. Why bother?"

1. Siloed tooling — Jira's AI sees Jira. Not Confluence, not GitHub, not your chat. Multi-source reasoning is impossible.

2. Lagging models — SaaS-bundled AI upgrades to frontier models 6-12 months late. Today on Claude.ai we use Opus 4.7; the Jira AI is likely on a GPT-4-class model.

3. No prompt customization — You can't enforce read-only, mandate citation, or impose team tone.

SaaS AI is fine for "summarize this ticket". Cross-tool agentic workflows hit a ceiling fast.