The Future: Dynamic On-The-Fly Harnesses

Tejas Kumar closes his talk with a vision of where harness engineering is headed ¹.

The Timeline

Dynamic Harnesses

The next step: an agent, given a task like “buy me a flight ticket,” first generates its own harness. Before doing the work, the agent creates the scaffolding — self-aware, it knows where it might hallucinate, where it might need guardrails, and where a verify step would catch failures.

Tejas describes this as “plan mode on steroids” ¹:

1. Analyze the task
2. Identify likely failure modes
3. Generate guardrails (max steps, context limits)
4. Generate tool definitions
5. Generate verify steps
6. Execute the task within the generated harness
7. Return the result

“This is honestly the next logical step towards AGI.” — Tejas Kumar ¹

This aligns with OpenAI’s observation that building reliable agents is about “designing environments, specifying intent, and building feedback loops” ².

What This Means for Engineers

The trend is clear: the competitive advantage in AI shifts from who has the best model to who can build the best harness. The model is rented and interchangeable. The harness is owned and differentiated.

Engineers should invest in:

• Tool design: what primitives does the agent need?
• Context strategy: what information at what time?
• Guardrail patterns: what are the hard limits?
• Verify logic: how do we catch failures deterministically?
• Environment management: how do we ensure isolation and cleanup?

The model is a commodity. The harness is the moat.

1. “Harnesses in AI: A Deep Dive” — Tejas Kumar, AI Engineer World’s Fair, May 2026 ↩ ↩2 ↩3

2. “Harness engineering: leveraging Codex in an agent-first world” — OpenAI, February 2026 ↩