The more you constraint an AI agent, the higher is its success rate. Have multiple agents run in parallel in a cluster to reduce the scope for each agent, this doesn't guarantee the 100% success rate, but you will find it on the higher end of the spectrum.
If you are trying to build an agent for deterministic tasks, it is bound to fail sooner or later. This happens because AI can hallucinate, lose context and it is probabilistic in nature. Its a good use case if the 90-95% of success rate still helps you.
If you’re building for probabilistic tasks, you’re much more likely to get useful outcomes.
Where it works well:
Writing documentation
Summarizing Emails
Fetching important details from messy data
Drafting sales emails and outreach messages
Analyzing support tickets for patterns
Where it doesnt work well:
Final financial reporting (P&L, tax filings)
Payroll calculations
Regulatory filings
Banking transactions or ledger updates
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