TargetBoard Agents

The Problems You Find Late Are Already Visible Today

TargetBoard agents watch your operational data continuously and raise what matters, with the context you need to act before it reaches the business.

Dashboards Wait to Be Asked. Agents Speak First.

Reporting answers the question you thought to ask. An agent watches continuously and tells you what you should be asking about.

Passive — you go and look

Cycle time, trending up.
AI usage, adopted by 44% of engineers.
Attrition risk, elevated in two teams.

Accurate, and still waiting for someone to open it. Nothing moves until a person notices the change, joins it to the other systems, and decides it deserves attention.

Proactive — it comes to you

Cycle time on Platform is up 31%, and the delay is in review, not development. Two reviewers carry 58% of the queue.
AI-authored changes on Mobile revert 2.4x more often than the baseline. No other team shows the pattern.
The two teams carrying attrition risk both own components in the Q4 renewal release.

An agent connects the signals, decides they are material, and puts the conclusion in front of the person who can change the outcome.

Meet Our Agents

Five domain experts, each watching one part of the operation. Select one to see the kind of signal it raises and the evidence behind it.

Software Quality Expert Example signal Monitoring

Continuously monitors engineering quality signals, highlighting rising defects, regressions, and production instability before they impact customers or delivery.

Escaped defects on Checkout have risen for three consecutive releases, and they trace to one service.

Hotfixes
4 in 14 days
All on Payments-API
Review depth
−41%
Comments per changed file, two repos
Defect origin
61%
Of Checkout defects, one service
Recommended action Hold the 4.3 release gate and assign a stability owner to Payments-API before the next scheduled deploy.
A dashboard would have shown: Defect count, above target.
Velocity Expert Example signal Monitoring

Tracks development speed across teams, surfacing slowdowns in cycle time, reviews, bug fixes, and delivery flow to keep execution moving.

Cycle time on Platform is up 31%. The delay is in review, not development.

PR wait time
2.1 → 4.6d
Time to first review
Review load
58%
Carried by two reviewers
Blocked work
9 PRs
On the same dependency
Recommended action Redistribute review ownership on Platform and unblock the shared dependency before the next planning cycle.
A dashboard would have shown: Cycle time, trending up.
AI Impact Expert Example signal Monitoring

Measures how AI adoption influences velocity, quality, and efficiency, revealing whether AI investments are truly accelerating performance and outcomes.

AI-assisted work tripled on Mobile. Rework grew with it, and only on that team.

AI-authored changes
44%
Of merged volume on Mobile
Revert rate
2.4×
Versus the team baseline
Token spend
+$18K/mo
No matching throughput gain
Recommended action Review the Mobile prompt and review policy before extending the licence expansion to the rest of engineering.
A dashboard would have shown: AI adoption, 44% of engineers.
Predictability Expert Example signal Monitoring

Analyzes planning reliability and delivery consistency, identifying scope creep, at-risk initiatives, and gaps between commitments and actual outcomes.

Search Revamp will miss its September commitment. The cause is scope, not capacity.

Scope added
+38%
After kickoff, none re-planned
Milestones re-dated
3 of 5
Each moved inside the sprint
Upstream slip
Identity
Slipped twice, not in the plan
Recommended action Re-baseline Search Revamp or cut the two additions made after kickoff. Raise the Identity dependency at the next steering review.
A dashboard would have shown: Initiative status, On Track.
DEV Experience Expert Example signal Monitoring

Monitors workload patterns and long-term DevEx signals, detecting burnout and attrition risks before they affect morale, retention, and productivity.

Two teams carrying elevated attrition risk both own components in the Q4 renewal release.

After-hours work
6 weeks
Sustained on Payments
Concentration
4 engineers
Above 20% after-hours commits
Exposure
Q4 renewal
Components owned by both teams
Recommended action Rebalance ownership on Payments and confirm coverage for the renewal release before the holiday freeze.
A dashboard would have shown: Attrition risk, elevated in two teams.

How an Agent Reaches a Conclusion

No black boxes. Every signal is reliable and can be traced back to the data it came from.

Your systems Jira GitHub Workday Zendesk Salesforce 100+ integrations
01

Connect

Engineering, delivery, HR, support and revenue systems land in one governed semantic layer. No prep work, even when the data is messy.

02

Monitor

Each expert agent watches its own domain continuously against your definitions, baselines and thresholds, not a generic industry average.

03

Correlate and prioritize

A single moving metric is noise. The agent holds it against the other systems, evaluates how material the change is, and ranks it against everything else.

04

Speak up

The conclusion arrives where you already work, with its evidence attached and every number traceable back to source.

Two Ways to Stay in Control

Reactive — you know what you want

Ask, and get an answer

Dashboards, natural-language answers and rule-based alerts, available in the app or through the MCP server in the assistant you already use.

Proactive — we know what you need

Let the answers find you

Expert agents scan your data on their own schedule and speak up when something crosses the line, including the things nobody thought to ask about.

Ways Teams Use TargetBoard Agents

TargetBoard agents act as personal assistants for managers, keeping them informed, removing blind spots, surfacing risks earlier, and pushing teams toward better results.

Track Execution

Monitor initiatives, milestones, and commitments to understand whether critical work is on track.

Get Answers

Get instant answers about performance, trends, and organizational health using natural language.

Detect Emerging Risks

Identify delivery, quality, planning, and operational risks before they become business problems.

Scale AI Adoption

Identify where AI is driving results or adding noise, and uncover opportunities to scale successful adoption.

Start With a Free 30-Day AI Operational Integrity Review

Your agents run on your data for 30 days and report what they find.

Book a Demo