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World Congress 2026 Europe • Jul 9, 2026 • Session details

When Should You Use an Agent? Architectural Trade-offs in Agentic Systems

Matheus Guimaraes

The era of coding line-by-line is over. Learn when to selectively replace rigid deterministic workflows with dynamic AI agents. Master the architectural trade-offs of autonomous systems.

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#1 about 5 min

Moving from predictable execution to autonomous agentic decision-making

Agentic architectures move beyond predictable code sequences to runtime multi-step workflows driven by intent.

#2 about 1 min

Simplifying tool integration with the Model Context Protocol

The Model Context Protocol creates a standardized layer connecting large language models to discrete microservices.

#3 about 2 min

Collaborative reasoning limits within the agent-to-agent protocol

Agents utilize the A2A protocol for chat-based collaborative reasoning while introducing new latency and turn-limit concerns.

#4 about 3 min

Applying architectural frameworks to AI development constraints

AI code generation accelerates implementation but requires developers to increasingly prioritize design records and structural tradeoffs.

#5 about 7 min

Evaluating traditional microservices routing within a baseline application

A mock store application provides a baseline example of traditional routing utilizing independent backend databases.

#6 about 3 min

Constraining natural language discovery via retrieval-augmented generation

Natural language searches require retrieval-augmented generation to ensure algorithms only recommend products found strictly within local inventory environments.

#7 about 2 min

Optimizing front-end application latency during generative operations

Deploying generative caching bounded by deterministic parameters preserves fluid and responsive user interface experiences.

#8 about 6 min

Implementing semantic search flows utilizing Amazon Bedrock foundation models

Fully managed vector databases and embedding models retrieve localized data structures before engaging external foundation models.

#9 about 2 min

Improving data relevance using granular chunking strategies for vectors

Mapping single entities strictly to individual vector chunks prevents context dilution and enhances semantic result accuracy.

#10 about 3 min

Enforcing deterministic application outputs through strict JSON prompts

Defining strict JSON schemas and utilizing mandatory tool integrations ensures large language models yield highly predictable logic paths.

#11 about 2 min

Balancing autonomous complexity using the hierarchy of agents pattern

Specialized subagents guided by overseeing parent modules replicate organizational structures to maintain efficiency during compound operations.

Matching moments

1:32 min

Architectural patterns for developing robust generative AI applications

Julián Duque Julián Duque · WWC 2025

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · WWC 2025

1:30 min

Transitioning from machine learning models to complex agentic systems

Alejandro Saucedo Alejandro Saucedo · WWC 2025

5:37 min

Architectural patterns for composing dynamic AI agents

Philipp Schmid Philipp Schmid · WWC 2025

1:40 min

Applying event-driven architecture to AI agent communication

diabhey diabhey · WWC 2025

2:02 min

Redefining the software architect role for AI pipelines

Ingo Eichhorst Ingo Eichhorst · WWC Europe 2026

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