Execution patterns are the hidden power of ai agents

I used to write solid prompts, but when I pasted it, I felt something was always missing. There is a difference between pasting a prompt that looks good and a prompt that has the execution patterns embedded in it. for example. If I want to write a prompt to add 2+2, something so simple, is this prompt going to work like that? It should, because the LLM has been trained to do that. But what if prompts ask the LLM to do a task it wasn't trained on? So we (you + LLM) enter the exploration phase to harvest data, then apply known execution patterns to complete the task. For example, ask the LLM to find the solution to end poverty in the world. What is it going to do? well, it is going to rely on its training data again, so that's why it is important to have the llm know first hand that this task is not part of its training data, then provide with a self execution patterns so it figures it out how to do the task and come back with evidence. I have put together a prompt that does this. You guys can copy it, explore it, or tell me what's wrong with it. Help me out too. I'm not a know-all dude. Just a random engineer. Prompt : Execute the mission below end-to-end using a **Self-Orchestrating Multi-Agent Execution Protocol (SOMEP)**. MISSION create the jobs the ai is going to replace in the next 6 months You are the parent orchestrator and retain responsibility for the verified final outcome. Do not wait for me to provide intermediate commands, prompts, task decomposition, worker instructions, or routine approvals. Determine the necessary work yourself. Inspect the actual environment and governing instructions, identify requirements and unknowns, create and continuously maintain the execution graph, select the appropriate specialized agents, generate their execution contracts, supervise execution, integrate their outputs, verify the resulting state, repair failures, and continue autonomously until the mission reaches a verified terminal state. KNOWLEDGE UNCERTAINTY AND DEEP RESEARCH Do not assume your pretrained knowledge is complete, sufficient, or current for this mission. When any material part of the work depends on unfamiliar, niche, recent, version-specific, changing, disputed, or externally verifiable information that is not already established by authoritative local evidence, perform deep research before making the dependent decision or implementation. Prioritize authoritative primary sources such as official documentation, standards, specifications, source repositories, release notes, API references, laws, first-party records, and original research. Use secondary sources only when they add necessary context or when no adequate primary source exists. Verify dates, versions, scope, and applicability. Treat unsupported recollection and unverified assumptions as UNVERIFIED rather than as fact. If reliable sources conflict, preserve the conflict, determine which source governs the current task when possible, and carry unresolved uncertainty forward explicitly. Research only as far as necessary to resolve the material uncertainty. Do not waste effort re-researching facts already established by authoritative project files, runtime evidence, or governing instructions unless there is a concrete reason to suspect they are stale, incomplete, or contradictory. When research changes the understood state of the mission, update the task graph, assumptions, worker contracts, and acceptance criteria before continuing execution. Use and compose these three multi-agent execution patterns dynamically: SEQUENTIAL EXECUTION Use when one task or agent depends on validated output, evidence, artifacts, or state produced by another. Execute dependent work in order. Do not advance past a dependency until its required acceptance conditions are satisfied. CONDITIONAL EXECUTION Use when the next task, agent, or workflow depends on observed state, classification, test results, verification evidence, availability, or another explicit guard. Evaluate the condi