BTP & Integrationintermediate

AI Agent Hub Part 3: Dashboard — KPIs, Business Value and Agent Performance

Key takeaway

Measure what agents are actually delivering: dashboards showing usage, business value and performance across the agent landscape, distinguishing agents that deliver measurable value from those that exist but aren't used, and how this visibility supports both governance and investment decisions.

Explanation

📌 KEY POINTS
✅ A central dashboard can show usage and performance across the entire agent landscape — not just SAP-built agents, but every registered agent regardless of vendor.
✅ Business value measurement distinguishes agents genuinely improving outcomes (faster resolution, reduced manual work) from agents that exist and are governed but rarely used.
✅ Usage and value data feed investment decisions: which agent patterns to invest in further, which to retire, which to extend.
✅ Cross-vendor comparison becomes possible once agents from different platforms are visible in one place — previously invisible because each platform reported separately, if at all.
⚠️ A dashboard showing only technical uptime, without business value or usage context, tells you an agent is running — not whether it's worth running.

━━━━━━━━━━━━━━━━━━━━
▶ WHERE WE ARE
Part 2 covered active governance. Now measuring value.

━━━━━━━━━━━━━━━━━━━━
▶ 1. USAGE METRICS

Who's calling which agent, how often.

━━━━━━━━━━━━━━━━━━━━
▶ 2. BUSINESS VALUE

Outcomes, not just technical health.

━━━━━━━━━━━━━━━━━━━━
▶ 3. UNDERUSED AGENTS

Governed but rarely used — a signal to investigate or retire.

━━━━━━━━━━━━━━━━━━━━
▶ 4. INVESTMENT DECISIONS

Data-driven, not anecdotal.

━━━━━━━━━━━━━━━━━━━━
▶ 5. CROSS-VENDOR VISIBILITY

Comparison across previously siloed platforms.

━━━━━━━━━━━━━━━━━━━━
▶ STEP BY STEP

Review usage metrics across the agent inventory.
Identify agents with measurable business value versus those rarely used.
Use this data to inform a retire/invest/extend decision for at least one agent.

✅ CHECKPOINT: you can point to a specific agent and justify, with dashboard data, whether to keep, extend or retire it.

Code example

ABAP Code

===== DASHBOARD SIGNALS =====High usage + high business value → invest further, consider extendingHigh usage + unclear value → instrument better, measure outcomesLow usage + high governance overhead → retirement candidateCross-vendor comparison → previously invisible without a central dashboard ===== VALUE VS UPTIME =====Technical uptime: "the agent responded when called" (necessary, not sufficient)Business value: "the agent measurably improved an outcome" (the real question)

Real project scenario

A company had a well-governed, technically reliable agent that had been running for months with almost no usage after its initial pilot users moved on. Because it appeared "healthy" on technical dashboards, no one had flagged it — until the AI Agent Hub dashboard's usage view made the low adoption visible, prompting a decision to retire it rather than keep maintaining its governance overhead for negligible value.

Common mistakes

Equating technical uptime with business value.
Not tracking usage over time, missing declining adoption.
Keeping governed-but-unused agents running indefinitely by default.
Judging agents anecdotally instead of with dashboard data.
Missing cross-vendor comparison opportunities.

Best practices

Track business value separately from technical uptime.
Monitor usage trends, not just point-in-time snapshots.
Use low usage plus governance overhead as a retirement signal.
Make investment decisions from dashboard data.
Compare agent performance across vendors where relevant.

Interview angle

Questions: "What's the difference between technical uptime and business value for an agent?", "How would you use dashboard data to decide whether to retire an agent?", "Why does cross-vendor visibility matter for investment decisions?"
Strong answer: usage vs value distinction, retirement signals, data-driven investment decisions, cross-vendor comparison value.

Practice this lesson

Quick check

1. What is the key difference between technical uptime and business value for an agent?

2. Which practice is risky when using agent dashboards for decisions?

3. What combination of dashboard signals suggests an agent is a retirement candidate?

Interview questions

1. Explain the purpose, end-to-end flow and key design decisions in "SAP AI Agent Hub with Joule Studio, LeanIX, Signavio and MCP".

Answer it in your own words first, then compare.

2. A project encounters a problem while implementing "SAP AI Agent Hub Governance: Verification, Risk and Lifecycle". What evidence would you check before changing configuration, code or architecture?