your AI agent needs an operating manual so first define its behavior a task tells an agent what to do while system prompt defines how it should operate find 20 leads is a task target criteria, approved sources, verification rules, failure handling, and output requirements are operating instructions use this 7 part framework to make yours role: What is the agent responsible for? objective: What outcome must it achieve? success criteria: What qualifies as a good result? workflow: What steps should it follow? constraints: What must it never do? tool policy: Which tools can it use, and when? output: What must the final result contain? design for failure specify what happens when data is missing, sources conflict, tools fail, or confidence is insufficient never let the agent invent facts or claim success without evidence separate instructions from enforcement a prompt saying never send without approval doesnt technically prevent an unauthorized action use permissions, access controls and human approval to enforce critical boundaries test it dont assume run three tests a normal task then a task with missing information then a task requiring refusal or human approval record pass or fail fix the biggest failure test again now you are ready to create write a system prompt for one task you repeat apply the framework and run those three tests measure reliability, not how impressive your prompt sounds a longer prompt isnt necessarily a better prompt better prompt eliminates ambiguity and produces more reliable behavior

you dont need to understand every neural network equation to build useful ai systems but you DO need to understand what an LLM can do what it cannot do and why it fails lets break it down what is an llm a large language model is a neural network trained to learn patterns in language and generate text from context at its core a standard language model learns to predict the next token give it the customer requested a refund because... it generates a continuation based on the context and patterns it learned that simple objective produces surprisingly powerful capabilities summarization coding translation extraction and more but fluent output isnt proof of truth tokens the units llms process llms dont read text exactly as humans do text is split into tokens whole words word fragments punctuation and other units why this matters context limits are measured in tokens not pages input and output tokens affect API costs longer prompts can increase latency and cost different languages and formats consume tokens differently context window ≠ permanent memory the context window is the working space available to a model for a request it can contain instructions conversation history documents tool definitions and generated output but three things are different context is the information available now memory is information retained or retrieved across interactions knowledge includes patterns learned during training plus information supplied at runtime a model may forget something from an earlier interaction if that information isnt included or retrieved why llms hallucinate llms can produce convincing statements that are false why? generating a plausible continuation and verifying a factual claim are not the same task if a model lacks reliable information it may still generate an answer that sounds authoritative this is dangerous in research finance customer support and autonomous workflows

my AI agent found the same flight for 40% less 900 bucks one click away but finding the deal isnt the same as earning permission to spend my money Id reject🔴 the transaction until the booking details are verified let AI do the work keep the final say #LedgerOp3n2026

most people use Ai few build a system that makes it reliable thats the difference between prompting a model and building an ai harness an ai harness is the system around a model that guides its work checks its outputs and controls what it can do it can include 5 layers → context give it the right information → memory retain relevant information across tasks → tools let it retrieve data or take action → verification check its outputs against clear criteria → permissions control what it can access and execute but the real advantage comes from connecting these layers into a feedback loop execute → verify → correct → retry dont just ask ai for an answer build a system that knows when its answer isnt good enough thats how you move from using ai to engineering more reliable ai systems

They went rogue they dominated the city and ruined everything meanwhile there were a small group of people @whereuncle @Niathecryp and i sentenced to protect the humanity's last barrier...