Keynote · 2026

Things you don't know about Agents

Loops, harness, context, memory: what actually moves the needle in production.
kami slides demo · A4 landscape · 7 slides
Agent LOOP
01
Agent Engineering
01 · Agent Loop
Simple core,
complex surroundings

The loop is small. The infrastructure around it is what keeps it stable as features grow.

  • A working Agent loop fits in about 20 lines of code.
  • Control flow lives in the tools, not in branchy internal state.
  • Workflow vs Agent: predefined paths in code vs model picks paths at runtime.
If your loop keeps growing every sprint, you are fixing the wrong layer. The tax is paid outside the loop.
TOOLS · HARNESS · EVALS · MEMORY PLAN ACT OBSERVE REFLECT 20 LOC CORE
02
Agent Engineering
02 · Harness
Harness wins
over hardware

More expensive models bring gains far smaller than expected. Verification, boundaries, feedback, and fallbacks matter more than model capability.

20
lines of code in a working Agent core loop
4
harness layers that matter: verify, bound, feedback, fallback
10×
velocity gains trace to execution discipline, not model swaps
03
Agent Engineering
03 · Context
Density beats length

Long context windows do not fix weak context design. Context Rot sets in around 300–400K tokens regardless of the model.

  • Layer the load: constant, on-demand, runtime, memory, system.
  • Index first, full content on demand. Beats dumping everything up front.
  • Stable prompt prefixes let caching actually pay off.
  • Every token that is not load-bearing is diluting signal.
LOADING STRATEGY vs ACCURACY Accuracy 85% 53% 53% Flat loading 85% Layered loading
04
Agent Engineering
04 · Tools & Memory
Put state outside
the context

Tools should match Agent goals, not underlying API shapes. Memory lives on disk, not in the window.

  • ACI principle: Agent-Computer Interface - design for what the Agent wants to do, not for the HTTP verb.
  • A bad tool description looks like a model failure until you re-read the description.
  • File-based state survives restarts. In-context state does not.

Four kinds of memory

  • Working: the context window - fast, expensive, temporary.
  • Procedural: SKILL.md files - how to behave, loaded lazily.
  • Episodic: JSONL logs - what happened, appendable.
  • Semantic: MEMORY.md - what to remember across sessions, consolidated at thresholds.
Cross-session consistency needs explicit consolidation, not hope.
05
Agent Engineering
05 · Code Style
Pseudocode over syntax

Comments should outnumber code lines. The reader sees logic, not a language tutorial.

  • Write the intent first, then the implementation detail below it.
  • Variable names describe what a thing is, not what type it has.
  • One concept per block. Split ruthlessly.
# resolve tool call or decide to stop function agent_step(context, tools): # model picks the next action action = model.think(context) # terminal condition: model says done if action.type == "stop": return action.result # delegate to the right tool result = tools[action.name](action.args) return agent_step(context + result, tools)
06
Agent Engineering

Protocol first.
Then parallelism.

Fix your evals before you tweak the Agent. Most of what looks like model failure is infrastructure noise in disguise.
07
End of Deck