09/29/2025
McKinsey studied 50 agentic AI builds and where they fail the most, and boiled it down to 6 key factors—essential for AI engineers:
1. It’s not about the agent, it’s about the workflow. don't obsess over building "impressive" agents. think about the whole system, not fun toys.
2. Agents aren’t always the answer.
Not every workflow needs a multi-agent system. Low-variance, predictable tasks are best handled with rules or ML, LLMs add complexity . The big wins for agents come in high-variance, messy processes (e.g. extract complex financial information)
3. Avoid "AI Slop". (common)
Focus on long-term development of agents, as you would with the development of an employee. Forget impressive demos. Double down on benchmarks. Agents should be given clear job descriptions, onboarded, and feedback so they improve regularly.
4. Track every step, not just outcomes.
Scaling agents up without visibility is asking for silent failures. Think about monitoring every stage of the workflow. This way teams detect errors early, refine logic quickly, and avoid total breakdowns. When mistakes happen (and they will), you can track where things went wrong and why. Don't skip this.
5. Reuse agents when you can.
Many companies waste time building one-off agents for each task. The smarter play is creating modular agent components (ingest, extract, verify, analyze) that can be reused for other workflows. Centralizing validated tools and prompts cuts 30–50% of redundant work, this number is no joke.
6. Humans remain essential, but in new roles.
Agents can parse, automate, and scale. But humans provide judgment, edge-case handling, and creative problem-solving. The future isn’t agent vs. human, but agent + human.
These are the mistakes startups and established companies make at scale.
They cause massive damage to reputation and resources.
And now you know how to avoid this.
- Northflow AI
