Lifecycle phase 12 of 13
Engineering intelligence: AI tools
Metrics on delivery, developer experience and AI impact.
7 tools list this as the main phase, and 5 more tools cover it. Each claim is the vendor's own words, and every fact on a tool's page links to its source.
Main phase
DXAtlassian“Developer intelligence platform designed by researchers to measure productivity, improve with data and navigate the shift to AI.”
FarosFaros AI“The token engineering platform: token intelligence, optimisation and governance across all your AI coding tools, tied to outcomes.”
JellyfishJellyfish“The intelligence platform for AI-integrated engineering: helps R&D teams understand impact, adopt AI and align work to the business.”
LinearBLinearB“Helps engineering leaders prove AI improves throughput without losing delivery confidence, with automations for agents, teams and managers.”
SpanSpan“AI-native engineering intelligence that links agent traces to code and PRs, with AI impact reporting and the Src agent.”
SwarmiaSwarmia“Engineering intelligence you can trust: shows why teams using the same AI tools get very different results.”
WaydevWaydev“AI-native engineering intelligence for AI ROI, DORA, SPACE and DX; AI measures, flags what matters and turns it into goals.”
Also covers this phase
Tools whose main phase is another one.
CortexCortex“Mission control for the AI software factory: visibility, governance and golden paths for engineering teams.”
Dash0 (Agent0)Dash0“OpenTelemetry-native observability with AI agents: watches the agents writing your code and fixes problems in production.”
MacroscopeMacroscope“AI code review, automated PR descriptions and real-time status updates on what the codebase is doing.”
PortPort“The agentic SDLC platform for building your own AI software factory, with context, agent management and governance.”
TemboTembo“Run any coding agent in the cloud: give every change its own computer, follow agent work, review results and see what merges.”