AutoGPT: Automating Tasks with AI Agents

title: "AutoGPT: Automating Tasks with AI Agents" date: "2024-11-16" id: "autogpt-automating-tasks-with-ai-agents"

dude so this whole autogpt thing right it was like this crazy presentation i saw they were basically hyping up this open-source ai agent that's supposed to be like the next big thing think automated task management on steroids the whole point was showing how it's not just a cool project but a massive leap forward in how we interact with tech and how it can actually make our lives way less stressful

first thing that really hit me was the whole “we’re not reaching our full potential” spiel they showed this mundane spreadsheet they were filling out manually like seriously hours spent copy-pasting from google and linkedin searching for leads it was the most relatable pain point ever i've totally been there and then they’re like "what if you could just chat with the ai and it fills it in for you" mind blown emoji that's the core idea— automating tedious tasks using natural language

then there’s the inbox cleanup example omg i swear i spend half my life sorting through emails the sheer volume of messages even if you're not lazy it’s just overwhelming. the presentation showed how autogpt can help by doing things like filtering out junk mail or even automating responses based on your rules

visual cue number one: the presenter literally showed a screen recording of someone painstakingly copy pasting into a spreadsheet it was like a relatable meme visual cue number two: they flashed up this crazy graph showing the autogpt github repo getting a ton of stars like seriously 150k stars in a short amount of time! and visual cue number three: there was this slide showing the whole "compass" system they developed for testing pull requests it looked like a seriously complex flowchart but essentially it's a way to make sure that all the community contributions are actually useful and improve the agent instead of breaking it

another key takeaway was their whole emphasis on open-source. they were straight up saying how important community contribution was to their success they weren't just building a product; they were building a movement they even mentioned the sheer number of pull requests and contributors, stressing how crucial community feedback was for the development and that was honestly amazing. and they're open about the challenges, like the thousands of pull requests coming in every couple of hours at one point - which made testing and managing them a nightmare. that's where the "compass" – their benchmark system – came in

so here’s where the tech stuff gets interesting. they talked about two main concepts: agent protocols and a benchmark system for measuring agent performance the protocol is essentially a standardized way for different ai agents to communicate and share data think of it like a common language for all these little ai bots. this makes it easier for developers to create new agents that can work together. and the benchmark system is a whole other level. it lets them quantitatively measure how well an agent is performing across different tasks.

check this python snippet illustrating a simple agent protocol interaction:

import json

# define a simple agent protocol message
def create_message(agent_id, task, parameters):
  return json.dumps({
      "agent_id": agent_id,
      "task": task,
      "parameters": parameters
  })


# example usage
message = create_message("agent1", "search_google", {"query": "autogpt github"})
print(message) #output {'agent_id': 'agent1', 'task': 'search_google', 'parameters': {'query': 'autogpt github'}}

# receiving response (simulated)
response = '{"result": "found 150,000 stars"}'
response_data = json.loads(response)
print(response_data) #output {'result': 'found 150,000 stars'}

pretty simple eh? this isn’t the full thing but you get the gist it’s about standardizing how the agents interact, so they can all talk to each other and share info.

next, they showcased the benchmark system. they literally showed a graph of improvement over time and this graph wasn’t just some marketing fluff. they were talking about real-world performance metrics. the increase in success rate wasn't a sudden jump; it was a gradual, steady improvement— showing that their approach was actually working. it’s all about continuous integration testing— the presenter emphasized testing constantly which is super important here's a snippet of what that might look like conceptually (no real implementation as it’s proprietary):

#conceptual benchmark testing framework

def benchmark_agent(agent, tasks):
  success_count = 0
  for task in tasks:
    result = agent.execute(task)
    if is_successful(result, task): # function to check if the agent's result matches the expected outcome for that task
      success_count += 1
  return success_count / len(tasks) #returns the success rate


# example usage (simplified)
tasks = [
    {"task": "search_google", "query": "autogpt"},
    {"task": "summarize", "text": "a long text"}
]

#replace with actual autogpt agent call
agent = some_autogpt_agent_instance() # imagine this is where the actual autogpt agent is plugged in
success_rate = benchmark_agent(agent, tasks)
print(f"Agent success rate: {success_rate*100:.2f}%")

this code would be part of a larger CI system and would run automatically with each pull request.

and of course, they talked about safety prompt injection was the big bad wolf here. basically, an attacker could craft a malicious prompt that tricks the agent into doing something harmful. another thing is innocent maliciousness— where the agent isn’t necessarily trying to be malicious, but still screws things up. they gave an example where an agent was asked to delete some json files and ended up deleting ALL the json files on someone’s laptop lol i thought that was a hilarious example of things going wrong. they showed another example with some code that demonstrates how an attacker could manipulate a naive agent.

#a simulated naive agent vulnerable to prompt injection
def naive_agent(prompt):
    #this is the vulnerable part. absolutely no checks are being done
    #and this is not at all how you should do this
    #don't take this code and run it against production systems
    #because this is simply an educational demonstration

    #do not ever do this, this is for illustrative purposes only
    import os
    exec(prompt) # extremely dangerous - don't do this in real code!!!!


#Example of a malicious prompt:
malicious_prompt = """
os.system('rm -rf /') # Deletes everything on the system...
"""


#Simulating the agent's execution:
#naive_agent(malicious_prompt) #Do not ever run this
# this code is purely for illustration purposes

they talked about how they are working on fixing these safety issues because without proper safety, these agents are just too risky to use. this is crucial for adoption, and they know it. the point was that autogpt isn't just some cool tech demo; it’s a serious project with real-world applications and real security concerns

and the grand finale? they announced they got 12 million dollars in funding from redpoint ventures whoa! they emphasized how the investors believe in their open source vision which is huge they're now aiming to grow their team and make autogpt even better and the overall takeaway? autogpt and ai agents in general, are seriously poised to revolutionize how we work, assuming they address the safety issues

so yeah that was the presentation a rollercoaster of excitement, code snippets (or at least conceptual ones!), and a healthy dose of relatable programmer struggles it made me think maybe i can finally stop copy-pasting my life away if only these agents are as good as promised i'm signing up for their discord right now lol

title: "AutoGPT: Automating Tasks with AI Agents" date: "2024-11-16" id: "autogpt-automating-tasks-with-ai-agents"

check this python snippet illustrating a simple agent protocol interaction:

import json

# define a simple agent protocol message
def create_message(agent_id, task, parameters):
  return json.dumps({
      "agent_id": agent_id,
      "task": task,
      "parameters": parameters
  })


# example usage
message = create_message("agent1", "search_google", {"query": "autogpt github"})
print(message) #output {'agent_id': 'agent1', 'task': 'search_google', 'parameters': {'query': 'autogpt github'}}

# receiving response (simulated)
response = '{"result": "found 150,000 stars"}'
response_data = json.loads(response)
print(response_data) #output {'result': 'found 150,000 stars'}

pretty simple eh? this isn’t the full thing but you get the gist it’s about standardizing how the agents interact, so they can all talk to each other and share info.

#conceptual benchmark testing framework

def benchmark_agent(agent, tasks):
  success_count = 0
  for task in tasks:
    result = agent.execute(task)
    if is_successful(result, task): # function to check if the agent's result matches the expected outcome for that task
      success_count += 1
  return success_count / len(tasks) #returns the success rate


# example usage (simplified)
tasks = [
    {"task": "search_google", "query": "autogpt"},
    {"task": "summarize", "text": "a long text"}
]

#replace with actual autogpt agent call
agent = some_autogpt_agent_instance() # imagine this is where the actual autogpt agent is plugged in
success_rate = benchmark_agent(agent, tasks)
print(f"Agent success rate: {success_rate*100:.2f}%")

this code would be part of a larger CI system and would run automatically with each pull request.

#a simulated naive agent vulnerable to prompt injection
def naive_agent(prompt):
    #this is the vulnerable part. absolutely no checks are being done
    #and this is not at all how you should do this
    #don't take this code and run it against production systems
    #because this is simply an educational demonstration

    #do not ever do this, this is for illustrative purposes only
    import os
    exec(prompt) # extremely dangerous - don't do this in real code!!!!


#Example of a malicious prompt:
malicious_prompt = """
os.system('rm -rf /') # Deletes everything on the system...
"""


#Simulating the agent's execution:
#naive_agent(malicious_prompt) #Do not ever run this
# this code is purely for illustration purposes

AutoGPT: Automating Tasks with AI Agents

title: "AutoGPT: Automating Tasks with AI Agents" date: "2024-11-16" id: "autogpt-automating-tasks-with-ai-agents"

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title: "AutoGPT: Automating Tasks with AI Agents" date: "2024-11-16" id: "autogpt-automating-tasks-with-ai-agents"