Engineering leadership

Scale AI across software delivery

Your developers already use Claude, Copilot, Cursor, and Codex. Bloomfilter shows what that changed about delivery, team by team: cycle time, rework, defect leakage, and cost per outcome, on one record from ticket to incident. Then you move from AI-assisted coding to agent-led delivery on evidence, not anecdotes.

Adoption by team
See which teams use agents, on what work, and what it changed.
Agent vs. human work
Compare agent-assisted work with the same team's work without agents.
Next bottleneck
Find where work piles up once coding gets faster.
Cost per outcome
Price each shipped outcome by team, tool, and model.

One event log from ticket to incident
Bloomfilter connects Jira or Azure DevOps, GitHub, CI/CD, ServiceNow, and agent telemetry from Claude, Copilot, Cursor, and Codex. The ticket, the agent sessions that worked it, the pull request, the release, and any incident it caused become one case, priced step by step.
Follow the bottleneck when it moves
Speed up coding, and review becomes the constraint. Clear review, and build time is next. Bloomfilter measures every step, for agents and for people, so you fix the step that holds the work today, not the one that held it last quarter.
Compare agent work with human-only work
Sessions bind to the work item, so agent-assisted cycle time, rework, and cost sit next to human-only work from the same team. That is how one software company read a spike in agent cycle time for what it was: agents taking on harder work.

From assisted coding to agent-led delivery

SDLC: assisted coding
Coding assistants help individual developers. Measure adoption and AI code share against a baseline rebuilt from the history already in your systems, so the before-state is real.
PDLC: AI across product
Agents write specs, tests, and docs alongside code. Measure the whole lifecycle, because the cost of a change often lands in a different stage than the one that created it.
AI-DLC: agent-led delivery
Agents take work from ticket to merged change, and people approve at checkpoints. Score every team bronze, silver, or gold on agentic usage and outcomes, and scale what works team by team.

Why it's hard

No two Jira setups match, and hundreds of teams run hundreds of variants of the same process. Bloomfilter discovers each team's process automatically and rolls it up from task to initiative to strategy, without asking anyone to standardize first.

Cycle time
From commitment to done, by stage, by team, with and without agents
Rework rate
Work that came back after it was called done, and where it came back from
Defect leakage
Bugs that got past review and test and became incidents
Say/do
What each team committed to against what it delivered
AI code share
How much of each merged pull request an agent wrote
Cost per outcome
What shipped, tested work cost, by team, tool, and model

Bloomfilter compared with the tools engineering teams use today
	Bloomfilter	Engineering metrics tools	Agent vendor dashboards	Value stream tools	Developer surveys
Agent activity across vendors	Yes	Yes	No	No	No
Agent sessions tied to the work item	Yes	No	No	No	No
Ticket to incident in one record	Yes	No	No	Yes	No
Baseline from pre-agent history	Yes	Yes	No	Yes	No
Independent of the AI seller	Yes	Yes	No	Yes	Yes