DH79 resources
AI Agent Implementation
How DH79 maps workflows, chooses pilots, builds agents, tests outputs, connects tools and monitors performance.
AI Agent Implementation
How DH79 Maps Your Workflows Before Building AI Agents
How should workflows be mapped before building agents? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
How to Choose the First AI Agent Workflow
Which AI agent workflow should we build first? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
The AI Agent Pilot: How to Prove Value Before Expanding
How do you pilot AI agents? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
How AI Agents Learn Your Business Context
How do AI agents learn company context? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
Agent Instructions, Memory and Tool Access Explained
What are agent instructions, memory and tool access? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
Model Routing for Business AI Agents: Use the Right Model for the Job
Which AI model should each agent use? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
How to Test AI Agents Before They Touch Real Work
How do you test agents before launch? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
How to Monitor AI Agent Performance After Launch
How do you monitor AI agents after launch? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
When to Add More Agents to an AI Agent Team
When should a company add more AI agents? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.
AI Agent Implementation
How to Turn Repetitive Business Tasks Into AI Agent Workflows
How do you convert tasks into AI workflows? In practice, AI agents are most useful when they take on repeatable knowledge work such as workflow mapping, pilot design, agent testing. The best implementations start with a narrow job, keep human approval where judgement matters, and improve the workflow once the team has seen real outputs.