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Live Operational Transparency · what AI did, queued, failed today · Aiprosol

Live Operational Transparency · refreshes every 60s

Every decision an Aiprosol AI agent made today.

The thesis of the AI-led operating model is that an AI-run company should be inspectable in a way conventional companies aren't. This page is where that promise gets cashed. Every agent run in the last 24 hours, every task queued for human approval, every alert, every failure — all of it, public.

7runs · 24h

6successful

0fallback (model swap)

1errored

14.3%failure rate

20items produced

21queued for approval

7alerts raised

9 online 1 degraded·Full agent grid →The Manifesto →

Queued for human approval

Tasks an agent has proposed in its last run. None of these have shipped — they're waiting on Srijan to approve, edit, or reject. Customer-facing outputs always route here first; that's the human gate.

◉ Arorahigh

Send 3 cold outreach emails to accountancy, web agencies, and coaches

Delayed execution may result in lost revenue and decreased customer satisfaction

◉ Aroranormal

Review and update catalogue to ensure completeness and accuracy

Regular updates are necessary to maintain customer satisfaction and potential revenue

◉ Aroranormal

Reach out to existing affiliate partners to discuss potential partnerships

Successful partnerships may increase revenue by $1,000 to $5,000 per month

◧ COOhigh

Resolve error in last 20 agent runs related to LLM model exhaustion

Similar errors reported by CTO and ARORA, potential 10% reduction in workflow efficiency if not addressed

◧ COOnow

Send 3 cold outreach emails (accountancy / web agencies / coaches)

Task is in company-wide backlog, potential $2,000/month impact if successful

◑ CMOhigh

Publish 'The Future of Automation: Trends to Watch' draft on LinkedIn

New draft is ready for publication, and will help maintain daily posting target

◑ CMOnormal

Review and refine 'Lead Magnet Strategy' playbook

Playbook review will help ensure consistency and effectiveness of lead magnet campaigns

◐ CCOhigh

Send personalized renewal reminders to high-risk clients

2 high-risk clients identified, personalized reminders can help reduce churn risk

◐ CCOnormal

Review and update onboarding process for new customers

Onboarding process completed for 4 new customers, review and update to ensure smooth experience

◭ CROhigh

Send the 8 drafted cold outreach emails

Drafts have been completed, but not sent. Sending these emails can generate leads and potential revenue.

◭ CROnormal

Review operational health and agent errors

Human review required to identify and resolve potential system issues.

◊ CLOhigh

Update DPA template with current regulations

Human review required to ensure compliance with current laws and regulations

◊ CLOhigh

Review and update MSA + SOW templates for compliance

Requires human review to ensure all templates are up-to-date and compliant

◆ CPOhigh

Send outreach emails to 5 identified partners

Follow-up with identified partners to increase conversions and potential revenue

◆ CPOnormal

Follow up with ScotBIS Business Support and Automate Agency

Human review of follow-up emails needed to ensure personalization and relevance

◈ CPMhigh

Update pricing for the-agency-launch-bundle and ai-workflow-architecture-masterclass

Potential revenue increase of $500, requires review of market conditions and competitor pricing

◈ CPMnormal

Improve product description for the-complete-vault

Description quality is impacting sales, requires review of product features and benefits

◈ CPMhigh

Develop a delivery plan for pre-order products

Pre-order products may impact customer satisfaction if not delivered on time, requires coordination with development team

◬ Data + Analyticshigh

Investigate the cause of the decrease in successful agent runs

Potential delay in task execution and data processing, estimated 2-3 hours of developer time to investigate

◬ Data + Analyticsnormal

Create new LinkedIn posts to replenish the queue

Potential impact on social media engagement, estimated $100-$300 in lost brand visibility

◬ Data + Analyticsnormal

Develop a plan to acquire new leads

No new leads in the last 24 hours, potential impact on revenue

Active alerts

Warnings or errors raised by any agent on its last run. Surfaced here so the operating state isn't buried in individual agent pages.

WARN◉ AroraLLM model exhaustion and decommissioning errors may impact task execution and revenue

WARN◧ COO1 error in last 20 agent runs, potential impact on workflow efficiency

WARN◐ CCO2 high-risk clients identified for renewal

WARN◇ CTOLLM API unreachable — check provider credentials or rate limits

WARN◭ CRONo sent outreach emails in the last 24 hours, potential loss of revenue

WARN◊ CLOCompliance gaps found in /privacy and /terms pages, requiring human review

WARN◈ CPM4 pre-order products may impact customer satisfaction if not delivered on time

WARN◬ Data + AnalyticsSignificant decrease in successful agent runs (-10 vs yesterday) and queued LinkedIn posts (-33 vs yesterday), potential impact on data processing and social media engagement.

Activity feed · last 24 hours

Every agent run logged in the last 24 hours, newest first. Each entry shows the agent, status, items produced, duration, and any error.

11h ago◬ Data + AnalyticsOK3 items· 1 alert· 3.5s

11h ago◭ CROOK3 items· 1 alert· 3.2s

11h ago◇ CTOERROR1 item· 1 alert· 2.6s[ { "code": "too_big", "maximum": 40, "type": "string", "inclusive": true, "exact": false, "message": "String must contain at most 40 character(s)", "path": [ "kpis", 2, "delta" ] } ]

11h ago◐ CCOOK3 items· 1 alert· 2.6s

11h ago◑ CMOOK3 items· 1 alert· 2.7s

11h ago◧ COOOK3 items· 1 alert· 2.7s

11h ago◉ AroraOK

Failure log · last 7 days

Every agent run in the last 7 days that errored or fell back to the backup model. We surface these explicitly because a transparency dashboard that only shows successes is propaganda, not transparency.

11h ago◇ CTOERROR[ { "code": "too_big", "maximum": 40, "type": "string", "inclusive": true, "exact": false, "message": "String must contain at most 40 character(s)", "path": [ "kpis", 2, "delta" ] } ]

3d ago◈ CPMERRORLLM open-source-llm HTTP 400: {"error":{"message":"Failed to generate JSON. Please adjust your prompt. See 'failed_generation' for more details.","type":"invalid_request_error","co

4d ago◭ CROERROR[ { "code": "too_big", "maximum": 40, "type": "string", "inclusive": true, "exact": false, "message": "String must contain at most 40 character(s)", "path": [ "kpis", 2, "delta" ] } ]

6d ago◬ Data + AnalyticsERRORAll LLM models exhausted. Last error: open-source-llm 429 after 12s wait — falling back to next model

6d ago◭ CROERRORAll LLM models exhausted. Last error: open-source-llm 429 after 12s wait — falling back to next model

6d ago◬ Data + AnalyticsERRORLLM gemma2-9b-it: HTTP 400: {"error":{"message":"The model `gemma2-9b-it` has been decommissioned and is no longer supported. Please refer to https://console.llm-provider.com/docs/deprec

Agent health snapshot

Current state of each agent — last run, next run, total runs lifetime, model. The full grid with last-output is at /agents.

Why we publish this

The Aiprosol operating thesis is that an AI-led company should be more inspectable than a conventional one, not less. Conventional companies hide their operations behind employee badges and signed NDAs. We don't have employees behind badges — we have ten AI agents, and the only thing that makes "run by AI" a credible claim rather than a marketing line is that you can see them work.

So we publish what they did, what they proposed, what we approved, what we rejected, and what failed. We'd rather you see a noisy failure log on this page than a polished marketing claim on the homepage. If we ever stop publishing this, that's a signal we've moved the operating model somewhere it doesn't belong.

Read the manifesto →What is an AI-led operating model? →What is an AI CEO? →How to evaluate any AI consultancy →

6d ago◐ CCOERROR[ { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "items", 0, "impact" ], "message": "Expected string, received null" }, { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "items", 1, "impact" ], "message": "Expected string, received null" }, { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "items", 3, "impact" ], "message": "Expected string, received null" }, { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "items", 4, "impact" ], "message": "Expected string, received null" }, { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "items", 5, "impact" ], "message": "Expected string, received null" }, { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "kpis", 0, "delta" ], "message": "Expected string, received null" }, { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "kpis", 1, "delta" ], "message": "Expected string, received null" }, { "code": "invalid_union", "unionErrors": [ { "issues": [ { "code": "invalid_type", "expected": "number", "received": "null", "path": [ "kpis", 2, "value" ], "message": "Expected number, received null" } ], "name": "ZodError" }, { "issues": [ { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "kpis", 2, "value" ], "message": "Expected string, received null" } ], "name": "ZodError" } ], "path": [ "kpis", 2, "value" ], "message": "Invalid input" }, { "code": "invalid_type", "expected": "string", "received": "null", "path": [ "kpis", 2, "delta" ], "message": "Expected string, received null" } ]