fatty

content writer

[fatty @fathy_sol]

𝐈𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐛𝐞𝐭𝐭𝐞𝐫 𝐭𝐨𝐨𝐥 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧

As AI agents gain access to more tools, selecting the right one becomes increasingly difficult. what works in a small prototype often becomes inefficient in production, where agents interact with many APIs, data sources and services.

Recent engineering work highlights a key challenge.. as tool libraries grow, agents struggle to consistently choose the most relevant option. performance therefore depends not only on model capability, but also on how tools are structured, filtered, and executed.

𝐇𝐨𝐰 𝐭𝐨𝐨𝐥 𝐠𝐫𝐨𝐰𝐭𝐡 𝐚𝐟𝐟𝐞𝐜𝐭𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞

ReAct-style agents follow loop:

think → choose tool → review the result → repeat

This process works with few tools but as the tool set expands, the model must evaluate many more options before deciding.

The often leads to:

⇒ incorrect tool selection
⇒ failed executions
⇒ additional reasoning cycles
⇒ slower response times

Instead of moving directly toward an answer, the agent spends time exploring unnecessary paths, reducing efficiency.

𝐍𝐚𝐫𝐫𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐬𝐞𝐚𝐫𝐜𝐡 𝐛𝐞𝐟𝐨𝐫𝐞 𝐦𝐚𝐤𝐢𝐧𝐠 𝐚 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧

One practical solution is to reduce the number of tools presented to the model.

In experiments shared by @SentientAGI , tools are first filtered for relevance before selection. rather than exposing everything, the system presents only the most relevant subset for a given request.

So instead of:

here are 40+ tools, choose one

the model sees:

here are the 10–15 relevant tools for this request.

With fewer options, selection becomes faster and more accurate, while unnecessary tool calls are reduced, especially for complex queries.

𝐌𝐚𝐤𝐢𝐧𝐠 𝐛𝐞𝐭𝐭𝐞𝐫 𝐮𝐬𝐞 𝐨𝐟 𝐭𝐢𝐦𝐞

Efficiency also depends on how tools are executed.

Many systems run tools sequentially even when tasks are independent but different data sources.. like market data, sentiment, and on-chain activity can often be fetched in parallel.

Running these simultaneously reduces waiting time and improves overall response speed.

𝐖𝐡𝐲 𝐭𝐡𝐞 𝐜𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐰𝐨𝐫𝐤𝐬

The strongest gains come from combining:

⇒ better tool filtering
⇒ parallel execution

Filtering reduces unnecessary choices, while parallel execution reduces delays. together, they help agents reach answers faster and with fewer wasted steps.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

Building effective AI agents involves more than improving the underlying model, it also requires designing systems that help the model use tools efficiently.

As tool sets expand, performance depends on selecting the right tools, reducing unnecessary actions, and optimizing how information is retrieved.

Ultimately, the goal is not to make agents do more work, but to help them do the right work. when tools are selected more carefully and used more efficiently, agents can reach useful answers faster and with fewer unnecessary steps.

[RUTH 🇨🇦 @it_Rutie]

As Ghanian artist no get superstars, see as Petrah cling to Wizkid.

[fatty @fathy_sol]

𝐈𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐛𝐞𝐭𝐭𝐞𝐫 𝐭𝐨𝐨𝐥 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧

As AI agents gain access to more tools, selecting the right one becomes increasingly difficult. what works in a small prototype often becomes inefficient in production, where agents interact with many APIs, data sources and services.

Recent engineering work highlights a key challenge.. as tool libraries grow, agents struggle to consistently choose the most relevant option. performance therefore depends not only on model capability, but also on how tools are structured, filtered, and executed.

𝐇𝐨𝐰 𝐭𝐨𝐨𝐥 𝐠𝐫𝐨𝐰𝐭𝐡 𝐚𝐟𝐟𝐞𝐜𝐭𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞

ReAct-style agents follow loop:

think → choose tool → review the result → repeat

This process works with few tools but as the tool set expands, the model must evaluate many more options before deciding.

The often leads to:

⇒ incorrect tool selection
⇒ failed executions
⇒ additional reasoning cycles
⇒ slower response times

Instead of moving directly toward an answer, the agent spends time exploring unnecessary paths, reducing efficiency.

𝐍𝐚𝐫𝐫𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐬𝐞𝐚𝐫𝐜𝐡 𝐛𝐞𝐟𝐨𝐫𝐞 𝐦𝐚𝐤𝐢𝐧𝐠 𝐚 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧

One practical solution is to reduce the number of tools presented to the model.

In experiments shared by @SentientAGI , tools are first filtered for relevance before selection. rather than exposing everything, the system presents only the most relevant subset for a given request.

So instead of:

here are 40+ tools, choose one

the model sees:

here are the 10–15 relevant tools for this request.

With fewer options, selection becomes faster and more accurate, while unnecessary tool calls are reduced, especially for complex queries.

𝐌𝐚𝐤𝐢𝐧𝐠 𝐛𝐞𝐭𝐭𝐞𝐫 𝐮𝐬𝐞 𝐨𝐟 𝐭𝐢𝐦𝐞

Efficiency also depends on how tools are executed.

Many systems run tools sequentially even when tasks are independent but different data sources.. like market data, sentiment, and on-chain activity can often be fetched in parallel.

Running these simultaneously reduces waiting time and improves overall response speed.

𝐖𝐡𝐲 𝐭𝐡𝐞 𝐜𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐰𝐨𝐫𝐤𝐬

The strongest gains come from combining:

⇒ better tool filtering
⇒ parallel execution

Filtering reduces unnecessary choices, while parallel execution reduces delays. together, they help agents reach answers faster and with fewer wasted steps.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

Building effective AI agents involves more than improving the underlying model, it also requires designing systems that help the model use tools efficiently.

As tool sets expand, performance depends on selecting the right tools, reducing unnecessary actions, and optimizing how information is retrieved.

Ultimately, the goal is not to make agents do more work, but to help them do the right work. when tools are selected more carefully and used more efficiently, agents can reach useful answers faster and with fewer unnecessary steps.

[fatty @fathy_sol]

@gtofweb3 i honestly don’t know wjen, they haven’t announced anything yet

[fatty @fathy_sol]

𝐈𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐛𝐞𝐭𝐭𝐞𝐫 𝐭𝐨𝐨𝐥 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧

As AI agents gain access to more tools, selecting the right one becomes increasingly difficult. what works in a small prototype often becomes inefficient in production, where agents interact with many APIs, data sources and services.

Recent engineering work highlights a key challenge.. as tool libraries grow, agents struggle to consistently choose the most relevant option. performance therefore depends not only on model capability, but also on how tools are structured, filtered, and executed.

𝐇𝐨𝐰 𝐭𝐨𝐨𝐥 𝐠𝐫𝐨𝐰𝐭𝐡 𝐚𝐟𝐟𝐞𝐜𝐭𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞

ReAct-style agents follow loop:

think → choose tool → review the result → repeat

This process works with few tools but as the tool set expands, the model must evaluate many more options before deciding.

The often leads to:

⇒ incorrect tool selection
⇒ failed executions
⇒ additional reasoning cycles
⇒ slower response times

Instead of moving directly toward an answer, the agent spends time exploring unnecessary paths, reducing efficiency.

𝐍𝐚𝐫𝐫𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐬𝐞𝐚𝐫𝐜𝐡 𝐛𝐞𝐟𝐨𝐫𝐞 𝐦𝐚𝐤𝐢𝐧𝐠 𝐚 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧

One practical solution is to reduce the number of tools presented to the model.

In experiments shared by @SentientAGI , tools are first filtered for relevance before selection. rather than exposing everything, the system presents only the most relevant subset for a given request.

So instead of:

here are 40+ tools, choose one

the model sees:

here are the 10–15 relevant tools for this request.

With fewer options, selection becomes faster and more accurate, while unnecessary tool calls are reduced, especially for complex queries.

𝐌𝐚𝐤𝐢𝐧𝐠 𝐛𝐞𝐭𝐭𝐞𝐫 𝐮𝐬𝐞 𝐨𝐟 𝐭𝐢𝐦𝐞

Efficiency also depends on how tools are executed.

Many systems run tools sequentially even when tasks are independent but different data sources.. like market data, sentiment, and on-chain activity can often be fetched in parallel.

Running these simultaneously reduces waiting time and improves overall response speed.

𝐖𝐡𝐲 𝐭𝐡𝐞 𝐜𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐰𝐨𝐫𝐤𝐬

The strongest gains come from combining:

⇒ better tool filtering
⇒ parallel execution

Filtering reduces unnecessary choices, while parallel execution reduces delays. together, they help agents reach answers faster and with fewer wasted steps.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

Building effective AI agents involves more than improving the underlying model, it also requires designing systems that help the model use tools efficiently.

As tool sets expand, performance depends on selecting the right tools, reducing unnecessary actions, and optimizing how information is retrieved.

Ultimately, the goal is not to make agents do more work, but to help them do the right work. when tools are selected more carefully and used more efficiently, agents can reach useful answers faster and with fewer unnecessary steps.

[fatty @fathy_sol]

𝐄𝐯𝐨𝐒𝐤𝐢𝐥𝐥: 𝐇𝐚𝐫𝐛𝐨𝐫 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧

Harbor integration in EvoSkill is a system that enables AI agents to be evaluated in real executable environments rather than static text-based benchmarks.

It connects agents to containerized tasks where they must produce working solutions that can be automatically tested.

𝐖𝐡𝐚𝐭 𝐇𝐚𝐫𝐛𝐨𝐫 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐃𝐨𝐞𝐬

Harbor Integration allows EvoSkill to:

⇒ run AI agents inside isolated containers (docker or daytona)
⇒ execute real programming or problem-solving tasks
⇒ automatically verify results using test cases
⇒ generate a performance score based on task success

𝐇𝐨𝐰 𝐇𝐚𝐫𝐛𝐨𝐫 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐖𝐨𝐫𝐤𝐬

The process follows a clear loop:

Step 1; task setup

Harbor uses benchmark tasks that include:

⇒ problem description
⇒ input/ output constraints
⇒ tests or verifier

Step 2; container execution

Tasks are executed in:

⇒ docker/ sandbox environments
⇒ or managed runtime systems like daytona (depending on setup)

Step 3; agent solving

The agent typically:

⇒ writes code
⇒ edits files
⇒ runs/debugs
⇒ iterates based on feedback

Step 4: automatic evaluation

The system evaluates the solution using test cases and returns a performance signal such as:

⇒ pass/fail outcome per test
⇒ or aggregated scores based on test success rate

𝐒𝐤𝐢𝐥𝐥 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐋𝐨𝐨𝐩

Harbor integration improves agents through feedback:

⇒ failed attempts are analyzed
⇒ weaknesses are identified
⇒ new reusable “skills” are created
⇒ future performance is improved

𝐊𝐞𝐲 𝐈𝐝𝐞𝐚 𝐁𝐞𝐡𝐢𝐧𝐝 𝐇𝐚𝐫𝐛𝐨𝐫 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧

Traditional AI evaluation:
Question → answer → score

Harbor-style evaluation:

Real task → code execution in sandbox → test verification → reward signal

This makes EvoSkill behave more like real-world software engineering, rather than just answering textbook-style questions.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞

Harbor integration is important because it:

⇒ tests agents in realistic execution environments
⇒ provides objective, test-based evaluation
⇒ enables continuous improvement through feedback
⇒ supports scalable benchmark integration

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

Harbor integration is a framework that evaluates AI agents by placing them in real execution environments, verifying their outputs through automated tests, and using the results to continuously improve their capabilities.

@SentientAGI

[fatty @fathy_sol]

@darmilarey069 Well written darmil

gSenti

[fatty @fathy_sol]

@gtofweb3 yes gt.. season 2

[@flashcastsocial @FlashcastSocial]

Just 2 hours and we are going live on youtube (link in comments) and X - @FlashcastSocial , @fermah_xyz .

Set your reminders to 10.30am ET today.

This marks the birth of a new category - every moment, every debate - you will be able to turn it into a market.

[fatty @fathy_sol]

@Emyfee_niks_ sis not bro .

[fatty @fathy_sol]

@gtofweb3 always focused

[fatty @fathy_sol]

@sly_d_s3c0nd Grand rising sly

[fatty @fathy_sol]

@Falalu83 @sleepagotchi Gm gm falalu

[fatty @fathy_sol]

@Cjtimzy @just_t00ns Gm gm timzy

[fatty @fathy_sol]

@Toluszn Gm gm tolu

[fatty @fathy_sol]

@Israel_tex Happy wednesday israel

[fatty @fathy_sol]

@eaziofweb3 @JupiterExchange @jup_predict Thanks for sharing eazi

[fatty @fathy_sol]

@gtofweb3 🚶‍♀️

[fatty @fathy_sol]

@cissey1919 @XOOBNetwork Gm hassan

[fatty @fathy_sol]

@JaphetzCruzz Gm gm japhet

[fatty @fathy_sol]

@Cryptosubrat @solskypilots @chiefdopefox @Companeons Good morning sot

[fatty @fathy_sol]

@OxMulla01 Gm al ameen