Multi-Agent LLM Design Tradeoffs
Splitting one agent into several buys context isolation, parallelism, and separate permissions, and costs tokens, handoff loss, and new failure paths. Measure it against a single-agent baseline.
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Evaluating Agent Actions and Workflow Outcomes
A final reply can be right while the world is wrong. Score agent runs by the effect ledger, constrain the trajectory, test faults and attacks, audit reviewers, and gate releases.
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Human Approval and Escalation in AI Workflows
Approval is a protocol, not a click. Define what the reviewer sees, who may approve, what invalidates an approval, what silence means, and what the audit records.
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Durable Agent Workflows
A crash or a lost response in the middle of a multi-step agent run leaves effects you cannot see. Journal steps, derive stable keys, treat unknown outcomes explicitly, and design compensation.
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Agent State, Planning, and Bounded Execution
An agent loop makes many individually valid calls. What keeps the sequence under control is explicit task state, a validated plan, budgets that end every run, and a trace that explains it afterward.
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Tool Authorization and Argument Validation
A model only proposes a tool call. The runtime decides whether it is well-formed, permitted for this user, valid in the current state, and safe to run without a person.