Agentic AI + Enterprise Workflow Automation
Agentic Planning Support for Product and Engineering Teams
Built an agentic workflow that surfaces the dependencies, trade-offs, and resourcing conflicts that normally only show up once a sprint is already underway.
Dependencies and constraints made visible before commitment rather than after
Trade-offs surfaced as explicit options instead of implicit assumptions
Planning context captured once and reused each cycle
Human sign-off kept as a required step in the workflow
The problem
Planning usually breaks down not because a team lacks information but because the information is scattered. Product definitions live in one place, feature requirements in another, and the constraints that actually decide sequencing sit in people's heads: who is available, what blocks what, and what committing to one thing costs somewhere else. The result is a plan that looks reasonable in the room and starts falling apart in week two, at which point the reasoning behind it is gone and nobody can reconstruct why the order made sense.
What I built
Wrote the product definitions, feature requirements, sprint goals, and the logic connecting them into structured context an agent could actually reason over, then built a workflow that uses it to surface dependencies, flag conflicts, and lay out the trade-off behind a sequencing decision. It is deliberately decision support and not decision making. The system makes constraints visible and argues for an ordering, and the team still decides. That boundary was the design, not a limitation, because a planning tool that quietly overrules people gets abandoned quickly.
Technical approach
- Planning knowledge is written down as structured context rather than prose, so the agent reasons over the actual relationships between goals, features, and constraints instead of over a summary of them
- Dependency and conflict detection runs against that structure, which is what lets it catch the second-order case where two individually reasonable commitments collide on the same person
- Trade-offs are presented as options with their consequences attached, so the output is something a team can argue with rather than a recommendation to accept or ignore
- The context is maintained as a living artifact, so each planning cycle starts from what the previous one learned instead of from a blank page
- Scope was held to what the system can support with evidence, because the fastest way to lose a planning team's trust is to be confidently wrong about a constraint they know better than you do