AI Weekly Malaysia

IBM Research's ALTK-Evolve framework lets agents self-distill reusable guidelines from past trajectories and inject them at inference time with no weight updates. Testing across eight models reveals agentic memory isn't a switch but a dose: strong models like DeepSeek-V3.2 (671B MoE) gain +9.5pp with the full guideline set, weaker models like gpt-oss-120b gain +16.1pp with selective retrieval at ~50% fewer tokens, and already-saturated models show no measurable gain.

Why: If you're building agents with memory injection, don't assume more context is always better — calibrate the dose to your model tier. For smaller or cheaper models, a curated core plus per-task retrieval is both more accurate and cheaper than dumping all guidelines in. Prompt caching makes even the full-set approach viable in production for frontier models with headroom.

Gartner forecasts agentic AI workflow costs will rise more than fivefold by end of 2028 because complex agent workflows consume far more tokens than chatbots, outweighing per-token price drops. Routing a single task to an agentic reasoning model increases inference costs at least fivefold, and usage-based billing models make runaway costs worse. Gartner separately predicted 40% of organizations will demote or decommission AI agents due to these and other problems.

Why: If you are building or budgeting AI agents, do not assume falling token prices will keep your costs flat—agent workflows that constantly reason, route, and self-question multiply token consumption. You should plan for model routing (assigning each task to the cheapest capable model) and set hard cost guardrails before deploying agents under usage-based billing, or expect bills to scale non-linearly with workflow complexity.