Artificial Intelligence & Next-Gen LLMs
AI Agents and Autonomous Swarms
AI agents go beyond chat by planning, using tools, and acting toward a goal. Swarms coordinate many agents on larger tasks. The promise is real automation, but so are the failure modes: loops, tool misuse, and runaway cost. Start small, bound the actions, and watch everything.
Pagrindiniai punktai
- Give each agent a narrow role and a clear stopping condition.
- Limit tool access to the minimum needed for the task.
- Add human approval gates for destructive or costly actions.
- Cap steps, tokens, and spend per run to prevent runaway loops.
- Log every tool call and decision for debugging and audit.
- Use deterministic tools for math, dates, and lookups.
- Test agents on adversarial and edge-case tasks.
- Start with one agent before orchestrating many.
Dažniausiai užduodami klausimai
Are agents reliable enough for production?
For bounded tasks with good tools and monitoring, yes; open-ended autonomy still needs oversight.
How do swarms avoid chaos?
Clear roles, shared state, and a coordinator that assigns and merges work.
What is the biggest risk?
Tool misuse and silent failures that look like success.
Do agents need their own memory?
Short-term scratchpads help; long-term memory needs careful curation.
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