Geliştirici

Prompt Trimmer

Uzun promptları token bütçesine indir — sınır bilinçli; sohbet geçmişi, doküman QA ve kod incelemesi için stratejiler.

Prompt girdisi

Bir prompt yapıştır, öncelikli paragrafları işaretle, sonra strateji seç.

Model:

832 token (tah.)3,279 karakter

# The State of AI Agents in 2026

Over the past three years, the conversation around large language models has shifted dramatically. What started as a fascination with chatbots and clever completions has matured into a serious engineering discipline focused on autonomous agents that can plan, reason, and act in the real world. This article surveys where we stand today.

## How we got here

The 2023 era of LLMs was defined by raw capability gains. Models doubled in context length, became multimodal, and began to genuinely follow instructions. By 2024 the bottleneck had shifted: capability was abundant but reliability was scarce. Hallucinations, brittle tool use, and the absence of long-term memory made it hard to deploy agents that mattered beyond demos.

A few key innovations broke the deadlock. Constitutional alignment and tool-use distillation gave us models that obeyed system prompts almost deterministically. Cheap, accurate token streaming made it possible to interrupt and steer running agents. And the proliferation of vector databases finally gave models the durable memory they had always needed.

## What an agent really is

It's tempting to define an agent as "an LLM in a loop", but that misses the point. A real agent is a system with goals, a planner, an execution surface, and accountability. The LLM is just the reasoning core. Modern frameworks separate these concerns cleanly: planners propose, executors act, judges verify, and memories persist. The interesting work happens in the seams between these pieces.

When teams ignore this structure, they end up shipping fragile prompt chains. The result is the kind of agent everyone has seen — confidently completes the first three steps, then quietly forgets what it was doing on step four. Treating agents as proper distributed systems, with retries, circuit breakers, and observability, is the line between a science project and a product.

## Token budgets matter more than ever

Modern context windows are huge, but using all of that space is rarely the right move. Long contexts increase latency, increase cost, and dilute the signal the model needs. A well-trimmed prompt — one that aggressively removes filler while preserving structure and intent — consistently outperforms its bloated cousin.

This is why prompt trimming has quietly become a core production discipline. The best agent stacks aren't the ones with the largest context windows; they're the ones that decide, turn by turn, what truly belongs in the context. Truncation strategies, paragraph-level pruning, density compression, and section-level summarization all sit in the same toolbox.

## What comes next

Looking ahead, we expect three trends. First, on-device inference will push more agents to the edge, making token efficiency a hard constraint. Second, multi-agent systems will become normal, requiring careful budget allocation across collaborators. Third, evaluation will mature: we'll stop benchmarking agents on toy tasks and start measuring real outcomes — work completed, time saved, errors prevented.

The agents that win this decade won't be the smartest in isolation. They'll be the most disciplined: lean prompts, clean memory, sharp tools, and a clear understanding of what they're really being asked to do.

Öncelik işaretleme modu

Paragrafları yüksek (koru) veya düşük (önce kırp) olarak işaretle.

Kırpma ayarları

Bütçeyi, stratejiyi ve neyin korunacağını seç.

Şablon

Strateji

Ortadan dışa doğru tam paragraflar atar.

Hedef token bütçesi

Çıktı için ayır

Modelin yanıt için yer bulabilmesi için bütçeden düşülür.

Korunacak yapılar

Başlıklar (#, ##)

Kod blokları (```)

Listeler (-, *, 1.)

Alıntı blokları (>)

Satır içi kod (`)

Kırpma işareti ekle

İçeriğin atıldığı yerlere [... 234 token kırpıldı ...] gibi işaret koy.

Kırpılmış çıktı

Anlık güncellenir. Nelerin atıldığını görmek için diff'i aç.

832 → 832

832

Kazanım

Korunan

100%

Atılanları göster

# The State of AI Agents in 2026

Over the past three years, the conversation around large language models has shifted dramatically. What started as a fascination with chatbots and clever completions has matured into a serious engineering discipline focused on autonomous agents that can plan, reason, and act in the real world. This article surveys where we stand today.

## How we got here

The 2023 era of LLMs was defined by raw capability gains. Models doubled in context length, became multimodal, and began to genuinely follow instructions. By 2024 the bottleneck had shifted: capability was abundant but reliability was scarce. Hallucinations, brittle tool use, and the absence of long-term memory made it hard to deploy agents that mattered beyond demos.

A few key innovations broke the deadlock. Constitutional alignment and tool-use distillation gave us models that obeyed system prompts almost deterministically. Cheap, accurate token streaming made it possible to interrupt and steer running agents. And the proliferation of vector databases finally gave models the durable memory they had always needed.

## What an agent really is

It's tempting to define an agent as "an LLM in a loop", but that misses the point. A real agent is a system with goals, a planner, an execution surface, and accountability. The LLM is just the reasoning core. Modern frameworks separate these concerns cleanly: planners propose, executors act, judges verify, and memories persist. The interesting work happens in the seams between these pieces.

When teams ignore this structure, they end up shipping fragile prompt chains. The result is the kind of agent everyone has seen — confidently completes the first three steps, then quietly forgets what it was doing on step four. Treating agents as proper distributed systems, with retries, circuit breakers, and observability, is the line between a science project and a product.

## Token budgets matter more than ever

Modern context windows are huge, but using all of that space is rarely the right move. Long contexts increase latency, increase cost, and dilute the signal the model needs. A well-trimmed prompt — one that aggressively removes filler while preserving structure and intent — consistently outperforms its bloated cousin.

This is why prompt trimming has quietly become a core production discipline. The best agent stacks aren't the ones with the largest context windows; they're the ones that decide, turn by turn, what truly belongs in the context. Truncation strategies, paragraph-level pruning, density compression, and section-level summarization all sit in the same toolbox.

## What comes next

Looking ahead, we expect three trends. First, on-device inference will push more agents to the edge, making token efficiency a hard constraint. Second, multi-agent systems will become normal, requiring careful budget allocation across collaborators. Third, evaluation will mature: we'll stop benchmarking agents on toy tasks and start measuring real outcomes — work completed, time saved, errors prevented.

The agents that win this decade won't be the smartest in isolation. They'll be the most disciplined: lean prompts, clean memory, sharp tools, and a clear understanding of what they're really being asked to do.

Geçmiş

Karşılaştırmak için bir snapshot kaydet.

İçerik türüne göre token başına karakter

İçerik türü	Karakter/token	Notlar
İngilizce düz yazı	≈ 4	Sohbet metni için varsayılan
Kod	≈ 3	Yoğun noktalama daha sık tokenize olur
CJK (中日韓)	≈ 2	Bir token çoğu zaman 1–2 glif
Sayı / ID	≈ 2.5	Rakam yoğun dizgiler sıkı tokenize olur
URL	≈ 3	Çok fazla noktalama

Yaygın bütçe biçimleri

Kısa sistem promptu: 200–600 token.
RAG bağlamı: istek başı 2k–8k.
Bir bölüm üzerinde QA: 8k–32k.
Tüm kod tabanı bağlamı: 100k–1M (Gemini, Claude uzun).
Modelin yanıtı için 300–800 token ayır.

Maliyet notu

Promptu 1k token kısaltmak, 1M input başına 3$ olan bir modelde çağrı başına ~0,003$ kazandırır — her çağrıyla katlanır.

AI maliyet tahminleyicisini aç →

Token sayıları yaklaşıktır. Gerçek tokenizer'lar modele göre değişir — bunları planlama için kullan, faturalandırma için değil.