The AI Native Dev - from Copilot today to AI Native Software Development tomorrow How Too Much Information Destroys Agent Performance
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Jan 20, 2026 Itamar Friedman, CEO of Qodo and an expert in multi-agent systems, joins Robert Brennan, CEO of OpenHands and AI orchestration specialist, to discuss the pitfalls of AI agent performance. They reveal that one-third of developer-reported AI output is incorrect and emphasize the critical difference between creative coding agents and structured review agents. Excessive information can hinder agent efficacy. Robert shares insights on scaling maintenance via cloud agents and breaking tasks into manageable parts, highlighting the need for human checks to build trust.
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Diversity Beats One-Size-Fits-All Agents
- Single-agent-with-tools is one approach but agents can differ deeply in architecture and permissions.
- Enterprise brownfield needs diverse agents with separate contexts, architectures, and access controls.
Use Different Agents For Coding And Review
- Separate creative coding agents from structured review agents with rigid checklists and rules.
- Use LLM-driven creativity for coding and rule-based graphs for security or code-review agents.
Context Determines Agent Quality
- Context selection is the main driver of agent quality; developers report 33–80% variance depending on tech and prompts.
- Too much, too little, or irrelevant context each cause failures, so fetch only the precise context needed.