[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]

@melissapan Avatar @melissapan Melissa Pan

Melissa Pan posts on X about ibm, intesa sanpaolo, year of, gain the most. They currently have XXXXX followers and X posts still getting attention that total XXXXXX engagements in the last XX hours.

Engagements: XXXXXX #

Engagements Line Chart

Mentions: X #

Mentions Line Chart

Followers: XXXXX #

Followers Line Chart

CreatorRank: XXXXXXX #

CreatorRank Line Chart

Social Influence

Social category influence stocks #5273 technology brands XXXXX%

Social topic influence ibm #7, intesa sanpaolo #1, year of 14.29%, gain 14.29%, prompt engineering 14.29%, secret 14.29%, has been XXXXX%

Top assets mentioned IBM (IBM) INTESA SANPAOLO (ISNPY)

Top Social Posts

Top posts by engagements in the last XX hours

"Thrilled to release our new paper MAP: Measuring Agents in Production ⚙🚀 2025 is the year of agents but do they actually work in the real world Is it just hype A group of XX researchers from Berkeley Stanford UIUC IBM and Intesa Sanpaolo investigated what makes agents deployable in the wild. So 📈 Why agents Productivity gains ➕ How to build production agents Simple & controllable methods 🧑💻 How to evaluate agents Heavy human oversight 🛑 Top challenge now Reliability remains unsolved We surveyed XXX agent builders and ran XX in-depth interviews across XX agent application domains to"
X Link 2025-12-05T16:12Z 3006 followers, 155.8K engagements

"(4/N) 📈 RQ1 - Why agents The primary usage of agents now is to augment human users for productivity and efficiency gain. XX% of the agents are built for human users Signal potentially heavy human oversights. 👓 We also observe an interesting trend of focus on latency lenient use cases 🐌: XX% of deployments tolerate response times of minutes or more while only XX% require sub-minute latencybecause even slow agents still beat human baselines. So AI agents are already running in production and creating real-world impact across XX diverse domains"
X Link 2025-12-05T16:27Z 3006 followers, 2926 engagements

"(5/N) ➕ RQ2 - How are production agents actually built We discover that simplicity and controllability win 🏆 💬70% of teams use off-the-shelf frontier models with no finetuning relying on prompting 👷79% of deployed agents lean heavily on manual prompt engineering (agent prompts can be 10k tokens) 🪡Workflows are short and controlled: XX% of agents run XX steps before a human steps in XX% X 🔧85% of case studies skip third-party agent frameworks and build custom apps In practice teams deliberately limit autonomy to keep agents reliable"
X Link 2025-12-05T16:47Z 3006 followers, 1854 engagements

"🚀 Building agents in real-world production Wed love to talk Were running a large-scale study on production agents - a collaborative effort from UC Berkeley IBM Stanford UIUC Intesa Sanpaolo and others. Join our study today I promise it only takes X mins Link in 🧵"
X Link 2025-08-28T14:45Z 3005 followers, 1000 engagements

"Its happening today 11am-2pm at Exhibit Hall CDE Poster XXX Come by our poster Ill tell you everything about MAST (and maybe our secret project too) 🤔"
X Link 2025-12-04T15:01Z 3004 followers, 13.7K engagements

"(6/N) 🧑💻RQ3 - how to evaluate agents in development humans are still heavily involved in the loop. 👀 XX% of deployed agents rely primarily on human-in-the-loop evaluation 🤖 XX% use LLM-as-a-judge but every in-depth case study teams that does this also adds human verification Public benchmarks rarely fit bespoke production tasks. 📊 XX% of teams build custom benchmarks dataset (with gold labels) XX% skip benchmark datasets entirely and lean on A/B tests expert review and user feedback Evaluation in the wild is a challenge domain-specific and still very human"
X Link 2025-12-05T16:51Z 3006 followers, 1869 engagements