Agentic DesignPatterns and tools for production AI agents

Loading...

📊

Adaptive Context Depth(ACD)

Dynamically adjust memory depth and context complexity based on task requirements in multi-agent agentic AI systems

Complexity: highContext Orchestration

Loading technique guide…

📊

Adaptive Context Depth(ACD)

Dynamically adjust memory depth and context complexity based on task requirements in multi-agent agentic AI systems

Complexity: highContext Orchestration

Loading technique guide…

Patterns

closed

Loading...

Loading Agentic Design…

Agentic DesignPatterns and tools for production AI agents

Expert help Pricing

Design Patterns & Techniques

Loading...

📊

Adaptive Context Depth(ACD)

Dynamically adjust memory depth and context complexity based on task requirements in multi-agent agentic AI systems

Complexity: highContext Orchestration

Core Mechanism

Dynamically scales context depth and memory allocation based on task complexity and runtime signals. Shallow depth prioritizes concise, high-salience summaries; deeper levels include richer evidence, cross-agent context, and longer retrieval spans. Practical enablers include adaptive attention spans, depth-adaptive/early-exit execution, and retrieval→rerank→summarize pipelines that keep only the most relevant tokens.

Workflow / Steps

Intake: classify task type, difficulty, novelty, and required modalities.
Estimate complexity: use signals (length, entropy, dependency graph, prior failures/evals).
Select depth policy: choose level (e.g., L1 facts → L5 system design) and per-agent budgets.
Assemble context: retrieve, rerank, deduplicate, and summarize; build shared context pools.
Execute: cap tokens/layers/calls; prefer early-exit when confidence is high.
Monitor: track cost, latency, attention concentration, retrieval quality, evaluator signals.
Adapt: expand/contract depth; update thresholds and caches for future similar tasks.

Best Practices

Retrieve → rerank (cross-encoder) → summarize; avoid naïvely stuffing long documents.

Set explicit token, layer, and tool-call budgets by depth; provide safe fallbacks.

Progressive disclosure: start shallow; expand on uncertainty or evaluator/test failure.

Cache summaries and retrieval results; deduplicate across agents with shared pools.

Tune chunk sizes/overlap by tokenizer; measure long-context effectiveness, not just size.

Use small models for routing/complexity scoring; reserve larger models for deep reasoning.

Guardrails: cap recursion/iterations; monitor cost/latency per successful task.

When NOT to Use

Uniform, simple tasks where a fixed shallow prompt meets quality and SLOs.
Hard real-time paths where retrieval/summarization overhead breaks latency budgets.
Strict auditability/compliance flows requiring deterministic, fixed-context inputs.
Extremely long contexts without reranking, where models under-utilize middle tokens.

Common Pitfalls

Stuffing long context without reranking → wasted tokens and marginal quality gains.
Cross-agent duplication and context drift; missing shared context pool.
No cost/latency guardrails; unbounded depth expansion and tool recursion.
Ignoring tokenizer effects; mis-tuned chunk sizes and overlap.

Key Features

Dynamic depth scaling with per-task/per-agent token budgets

Retrieval-aware context assembly with reranking and compression

Early-exit / depth-adaptive execution for efficiency

Cross-agent context deduplication and shared pools

Performance-aware adaptation from live metrics

Policy-driven expansion on uncertainty or evaluator fail

KPIs / Success Metrics

Task success/quality vs fixed-depth baseline; evaluator pass rate.
Tokens and cost per successful task; depth expansion rate.
Latency p50/p95 with and without expansion; throughput under load.
Retrieval hit rate and reranker MRR/NDCG; duplication and cache hit rates.

Token / Resource Usage

Budget by depth: shallow (summaries, top-k evidence) → deep (full passages, cross-agent pools).
Heuristics: rerank to top-k, summarize to target ratio, compress shared context, stream results.
Runtime: prefer paged/flash attention and efficient serving; cache summaries and retrieval results.
Accounting: attribute tokens/cost to the final successful task, not individual subcalls.

Best Use Cases

Multi-agent research and planning with highly variable difficulty and evidence needs.
Customer support triage/escalation with retrieval, reranking, and selective depth.
Long-document QA and compliance reviews with progressive disclosure.
Code assistance across large repos; expand depth on uncertainty or failing tests.

References & Further Reading

Academic Papers

Implementation Guides

Tools & Libraries

tiktoken (token counting)
vLLM (PagedAttention) for efficient long-context serving
FlashAttention
LangChain / LlamaIndex (retrieval, rerankers, compressors)

Community & Discussions

Loading technique guide…

📊

Adaptive Context Depth(ACD)

Dynamically adjust memory depth and context complexity based on task requirements in multi-agent agentic AI systems

Complexity: highContext Orchestration

Core Mechanism

Workflow / Steps

Intake: classify task type, difficulty, novelty, and required modalities.
Estimate complexity: use signals (length, entropy, dependency graph, prior failures/evals).
Select depth policy: choose level (e.g., L1 facts → L5 system design) and per-agent budgets.
Assemble context: retrieve, rerank, deduplicate, and summarize; build shared context pools.
Execute: cap tokens/layers/calls; prefer early-exit when confidence is high.
Monitor: track cost, latency, attention concentration, retrieval quality, evaluator signals.
Adapt: expand/contract depth; update thresholds and caches for future similar tasks.

Best Practices

Retrieve → rerank (cross-encoder) → summarize; avoid naïvely stuffing long documents.

Set explicit token, layer, and tool-call budgets by depth; provide safe fallbacks.

Progressive disclosure: start shallow; expand on uncertainty or evaluator/test failure.

Cache summaries and retrieval results; deduplicate across agents with shared pools.

Tune chunk sizes/overlap by tokenizer; measure long-context effectiveness, not just size.

Use small models for routing/complexity scoring; reserve larger models for deep reasoning.

Guardrails: cap recursion/iterations; monitor cost/latency per successful task.

When NOT to Use

Uniform, simple tasks where a fixed shallow prompt meets quality and SLOs.
Hard real-time paths where retrieval/summarization overhead breaks latency budgets.
Strict auditability/compliance flows requiring deterministic, fixed-context inputs.
Extremely long contexts without reranking, where models under-utilize middle tokens.

Common Pitfalls

Stuffing long context without reranking → wasted tokens and marginal quality gains.
Cross-agent duplication and context drift; missing shared context pool.
No cost/latency guardrails; unbounded depth expansion and tool recursion.
Ignoring tokenizer effects; mis-tuned chunk sizes and overlap.

Key Features

Dynamic depth scaling with per-task/per-agent token budgets

Retrieval-aware context assembly with reranking and compression

Early-exit / depth-adaptive execution for efficiency

Cross-agent context deduplication and shared pools

Performance-aware adaptation from live metrics

Policy-driven expansion on uncertainty or evaluator fail

KPIs / Success Metrics

Task success/quality vs fixed-depth baseline; evaluator pass rate.
Tokens and cost per successful task; depth expansion rate.
Latency p50/p95 with and without expansion; throughput under load.
Retrieval hit rate and reranker MRR/NDCG; duplication and cache hit rates.

Token / Resource Usage

Budget by depth: shallow (summaries, top-k evidence) → deep (full passages, cross-agent pools).
Heuristics: rerank to top-k, summarize to target ratio, compress shared context, stream results.
Runtime: prefer paged/flash attention and efficient serving; cache summaries and retrieval results.
Accounting: attribute tokens/cost to the final successful task, not individual subcalls.

Best Use Cases

Multi-agent research and planning with highly variable difficulty and evidence needs.
Customer support triage/escalation with retrieval, reranking, and selective depth.
Long-document QA and compliance reviews with progressive disclosure.
Code assistance across large repos; expand depth on uncertainty or failing tests.

References & Further Reading

Academic Papers

Implementation Guides

Tools & Libraries

tiktoken (token counting)
vLLM (PagedAttention) for efficient long-context serving
FlashAttention
LangChain / LlamaIndex (retrieval, rerankers, compressors)

Community & Discussions

Loading technique guide…

Patterns

closed

Design Patterns & Techniques

Loading...

Core Mechanism

Workflow / Steps

Intake: classify task type, difficulty, novelty, and required modalities.
Estimate complexity: use signals (length, entropy, dependency graph, prior failures/evals).
Select depth policy: choose level (e.g., L1 facts → L5 system design) and per-agent budgets.
Assemble context: retrieve, rerank, deduplicate, and summarize; build shared context pools.
Execute: cap tokens/layers/calls; prefer early-exit when confidence is high.
Monitor: track cost, latency, attention concentration, retrieval quality, evaluator signals.
Adapt: expand/contract depth; update thresholds and caches for future similar tasks.

Best Practices

Retrieve → rerank (cross-encoder) → summarize; avoid naïvely stuffing long documents.

Set explicit token, layer, and tool-call budgets by depth; provide safe fallbacks.

Progressive disclosure: start shallow; expand on uncertainty or evaluator/test failure.

Cache summaries and retrieval results; deduplicate across agents with shared pools.

Tune chunk sizes/overlap by tokenizer; measure long-context effectiveness, not just size.

Use small models for routing/complexity scoring; reserve larger models for deep reasoning.

Guardrails: cap recursion/iterations; monitor cost/latency per successful task.

When NOT to Use

Uniform, simple tasks where a fixed shallow prompt meets quality and SLOs.
Hard real-time paths where retrieval/summarization overhead breaks latency budgets.
Strict auditability/compliance flows requiring deterministic, fixed-context inputs.
Extremely long contexts without reranking, where models under-utilize middle tokens.

Common Pitfalls

Stuffing long context without reranking → wasted tokens and marginal quality gains.
Cross-agent duplication and context drift; missing shared context pool.
No cost/latency guardrails; unbounded depth expansion and tool recursion.
Ignoring tokenizer effects; mis-tuned chunk sizes and overlap.

Key Features

Dynamic depth scaling with per-task/per-agent token budgets

Retrieval-aware context assembly with reranking and compression

Early-exit / depth-adaptive execution for efficiency

Cross-agent context deduplication and shared pools

Performance-aware adaptation from live metrics

Policy-driven expansion on uncertainty or evaluator fail

KPIs / Success Metrics

Task success/quality vs fixed-depth baseline; evaluator pass rate.
Tokens and cost per successful task; depth expansion rate.
Latency p50/p95 with and without expansion; throughput under load.
Retrieval hit rate and reranker MRR/NDCG; duplication and cache hit rates.

Token / Resource Usage

Budget by depth: shallow (summaries, top-k evidence) → deep (full passages, cross-agent pools).
Heuristics: rerank to top-k, summarize to target ratio, compress shared context, stream results.
Runtime: prefer paged/flash attention and efficient serving; cache summaries and retrieval results.
Accounting: attribute tokens/cost to the final successful task, not individual subcalls.

Best Use Cases

Multi-agent research and planning with highly variable difficulty and evidence needs.
Customer support triage/escalation with retrieval, reranking, and selective depth.
Long-document QA and compliance reviews with progressive disclosure.
Code assistance across large repos; expand depth on uncertainty or failing tests.

References & Further Reading

Academic Papers

Implementation Guides

Tools & Libraries

tiktoken (token counting)
vLLM (PagedAttention) for efficient long-context serving
FlashAttention
LangChain / LlamaIndex (retrieval, rerankers, compressors)

Community & Discussions

Core Mechanism

Workflow / Steps

Intake: classify task type, difficulty, novelty, and required modalities.
Estimate complexity: use signals (length, entropy, dependency graph, prior failures/evals).
Select depth policy: choose level (e.g., L1 facts → L5 system design) and per-agent budgets.
Assemble context: retrieve, rerank, deduplicate, and summarize; build shared context pools.
Execute: cap tokens/layers/calls; prefer early-exit when confidence is high.
Monitor: track cost, latency, attention concentration, retrieval quality, evaluator signals.
Adapt: expand/contract depth; update thresholds and caches for future similar tasks.

Best Practices

Retrieve → rerank (cross-encoder) → summarize; avoid naïvely stuffing long documents.

Set explicit token, layer, and tool-call budgets by depth; provide safe fallbacks.

Progressive disclosure: start shallow; expand on uncertainty or evaluator/test failure.

Cache summaries and retrieval results; deduplicate across agents with shared pools.

Tune chunk sizes/overlap by tokenizer; measure long-context effectiveness, not just size.

Use small models for routing/complexity scoring; reserve larger models for deep reasoning.

Guardrails: cap recursion/iterations; monitor cost/latency per successful task.

When NOT to Use

Uniform, simple tasks where a fixed shallow prompt meets quality and SLOs.
Hard real-time paths where retrieval/summarization overhead breaks latency budgets.
Strict auditability/compliance flows requiring deterministic, fixed-context inputs.
Extremely long contexts without reranking, where models under-utilize middle tokens.

Common Pitfalls

Stuffing long context without reranking → wasted tokens and marginal quality gains.
Cross-agent duplication and context drift; missing shared context pool.
No cost/latency guardrails; unbounded depth expansion and tool recursion.
Ignoring tokenizer effects; mis-tuned chunk sizes and overlap.

Key Features

Dynamic depth scaling with per-task/per-agent token budgets

Retrieval-aware context assembly with reranking and compression

Early-exit / depth-adaptive execution for efficiency

Cross-agent context deduplication and shared pools

Performance-aware adaptation from live metrics

Policy-driven expansion on uncertainty or evaluator fail

KPIs / Success Metrics

Task success/quality vs fixed-depth baseline; evaluator pass rate.
Tokens and cost per successful task; depth expansion rate.
Latency p50/p95 with and without expansion; throughput under load.
Retrieval hit rate and reranker MRR/NDCG; duplication and cache hit rates.

Token / Resource Usage

Budget by depth: shallow (summaries, top-k evidence) → deep (full passages, cross-agent pools).
Heuristics: rerank to top-k, summarize to target ratio, compress shared context, stream results.
Runtime: prefer paged/flash attention and efficient serving; cache summaries and retrieval results.
Accounting: attribute tokens/cost to the final successful task, not individual subcalls.

Best Use Cases

Multi-agent research and planning with highly variable difficulty and evidence needs.
Customer support triage/escalation with retrieval, reranking, and selective depth.
Long-document QA and compliance reviews with progressive disclosure.
Code assistance across large repos; expand depth on uncertainty or failing tests.