proadhikary/MENST
Preview
•
Updated
•
145
•
2
P.M.SALMAN KHAN
salmankhanpm
AI & ML interests
None yet
Recent Activity
liked
a dataset
4 days ago
proadhikary/MENST
updated
a model
10 days ago
salmankhanpm/gemma-3-4b-it-ft
published
a model
10 days ago
salmankhanpm/gemma-3-4b-it-ft
Organizations
salmankhanpm's activity
liked
a
dataset
4 days ago
updated
a
model
10 days ago
published
a
model
10 days ago
updated
a
model
10 days ago
published
a
model
10 days ago
updated
a
dataset
10 days ago
published
2
datasets
10 days ago
updated
a
dataset
10 days ago
published
a
dataset
10 days ago
reacted to
hesamation's
post with 🔥
16 days ago
Post
2095
OpenAI just released a 34-page practical guide to building agents,
Here's 10 things it teaches us:
1➜ agents are different from workflows: they are complete autonomous systems that perform tasks on your behalf. many applications use LLMs for workflows, but this is not an agent.
2➜ use them for tricky stuff: complex decision making, dynamic rules, unstructured data
3➜ core recipe: each agent has three main components: Model (the brain), Tools, Instructions on how to behave
4➜ choose the right brain: set up evals to get a baseline performance, use a smart model to see what's possible, gradually downgrade the model for cost and speed
5➜ tools are key: choose well-defined and tested tools. an agent needs tools to retrieve data and context, and take actions.
6➜ instruction matters A LOT: be super clear telling the agent its goals, steps, and rules. Vague instructions = unpredictable agent. Be explicit.
7➜ start simple, then scale: often a single agent with several tools is ok. don't jump to complex multi-agent systems immediately.
8➜ if you use multi-agents: you can have a "manager" agent directing traffic to specialist agents, or have agents hand off tasks to each other.
9➜ gaurdrails are a MUST: check user input for weird stuff, make sure the agent isn't about to do something risky, filter out private info, block harmful content. Don't let it run wild.
10➜ build and plan for humans: start small, test, improve. always have a plan for when the agent gets stuck or is about to do something high-risk.
Download: https://t.co/fJaCkgf7ph
Here's 10 things it teaches us:
1➜ agents are different from workflows: they are complete autonomous systems that perform tasks on your behalf. many applications use LLMs for workflows, but this is not an agent.
2➜ use them for tricky stuff: complex decision making, dynamic rules, unstructured data
3➜ core recipe: each agent has three main components: Model (the brain), Tools, Instructions on how to behave
4➜ choose the right brain: set up evals to get a baseline performance, use a smart model to see what's possible, gradually downgrade the model for cost and speed
5➜ tools are key: choose well-defined and tested tools. an agent needs tools to retrieve data and context, and take actions.
6➜ instruction matters A LOT: be super clear telling the agent its goals, steps, and rules. Vague instructions = unpredictable agent. Be explicit.
7➜ start simple, then scale: often a single agent with several tools is ok. don't jump to complex multi-agent systems immediately.
8➜ if you use multi-agents: you can have a "manager" agent directing traffic to specialist agents, or have agents hand off tasks to each other.
9➜ gaurdrails are a MUST: check user input for weird stuff, make sure the agent isn't about to do something risky, filter out private info, block harmful content. Don't let it run wild.
10➜ build and plan for humans: start small, test, improve. always have a plan for when the agent gets stuck or is about to do something high-risk.
Download: https://t.co/fJaCkgf7ph
- 3 replies
updated
a
model
21 days ago
published
a
model
21 days ago
updated
a
model
21 days ago
published
a
model
21 days ago
updated
a
model
25 days ago
published
a
model
25 days ago
updated
a
model
27 days ago
updated
a
model
about 1 month ago