Agent Engineering
Context Compression: Four Layers of Defense
Drag the slider to simulate conversation growth and observe how compression extends context lifespan.
Four-Layer Compression Strategy
Safe Zone
Layer 1 · Snip
120K (60%)
-36K
Delete overly long raw data returned by early tools, keeping only summaries. Users notice nothing.
Before
Weather API returns a 1,200-Token JSON: 7-day hourly forecast…
→
After
Tool summary: Tomorrow in Beijing cloudy 12–20°C (80 Tokens)
Layer 2 · MicroCompact
150K (75%)
-50K
Replace early long conversations with brief summaries. Minor information loss, key information retained.
Before
Turn 3: "That file isn't PDF format, I need a Word version, change the title to…"
→
After
Summary: user requested Word format, title change, color adjustment
Layer 3 · Collapse
170K (85%)
-80K
Collapse multiple early conversation turns into a single summary message. Detail loss, but the main thread is preserved.
Before
Turns 1–8 (12 messages, 4,200 Tokens): discussed requirements, confirmed plan, revised 3 times…
→
After
Session summary: React + TS project, currently editing the reports page (350 Tokens)
Layer 4 · AutoCompact
190K (95%)
-110K
Full compression: retain only system + global summary + last 3 turns. Significant information loss, but prevents crash.
Before
20 complete turns (38 messages, 18,000 Tokens)
→
After
system + summary + last 3 turns (2,500 Tokens)
📌 Design Decision: Drag the slider and observe: without compression, a 200K context only supports one long conversation. With four-layer compression, the same window can sustain 5× or more conversation volume.
Takeaway
Takeaway Model window 256K, safe headroom 200K. Four-layer compression works like a flood-control dam: each time the window nears capacity, it automatically drains the water level, allowing the same window to carry far more than 200K of conversation. Only when truly nothing more can be deleted does it overflow.