Platform
One Request. Seventeen Possible Architectures. Always the Right One.
Agentica routes every task through the optimal combination of AI reasoning patterns — from simple tool calls to multi-agent specialist teams with built-in safety gates. Here is how it works, from the moment a request arrives to the moment a verified answer ships.
From Request to Response in Six Steps
Request Arrives
Your request hits the API gateway. Authentication is verified, input is sanitized, rate limits are checked — before any AI processing begins.
Memory Recall
The platform searches long-term memory for relevant context from past interactions, preferences, and organizational knowledge.
Architecture Selection
An intelligent controller analyzes the request and selects the optimal architecture pattern — or combines multiple patterns for complex tasks.
Agent Execution
The selected agent architecture processes your request. It may reason, call tools, consult specialists, simulate outcomes, or request human approval — depending on the pattern.
Quality Verification
Before delivery, every response passes through automated quality scoring across five dimensions: accuracy, helpfulness, relevancy, conciseness, and safety.
Response Delivered
Your verified response arrives via API or real-time stream. Memory is updated in the background for future context. Every step is traced for auditability.
Request Arrives
Your request hits the API gateway. Authentication is verified, input is sanitized, rate limits are checked — before any AI processing begins.
Memory Recall
The platform searches long-term memory for relevant context from past interactions, preferences, and organizational knowledge.
Architecture Selection
An intelligent controller analyzes the request and selects the optimal architecture pattern — or combines multiple patterns for complex tasks.
Agent Execution
The selected agent architecture processes your request. It may reason, call tools, consult specialists, simulate outcomes, or request human approval — depending on the pattern.
Quality Verification
Before delivery, every response passes through automated quality scoring across five dimensions: accuracy, helpfulness, relevancy, conciseness, and safety.
Response Delivered
Your verified response arrives via API or real-time stream. Memory is updated in the background for future context. Every step is traced for auditability.
Under the Hood
The business-friendly overview above tells you what happens. The sections below tell your technical evaluators how.
Composable Architecture Building Blocks
Most AI platforms offer a single approach to every problem. Agentica provides 17 distinct architecture patterns that can be mixed, matched, and stacked.
Each architecture is implemented as a LangGraph StateGraph — a directed graph of processing nodes with typed state management. State flows through nodes via TypedDict with Annotated reducers. The 17 architectures span five categories: Foundational (Reflection, Tool Use, ReAct, Planning), Multi-Agent (Specialist Teams, Blackboard, Meta-Controller, Ensemble), Memory & Reasoning (Episodic Memory, Tree of Thoughts, Graph Memory), Safety & Reliability (PEV, Mental Loop, Dry-Run, Metacognitive), and Learning & Adaptation (RLHF, Cellular Automata). Each pattern is composable because they share the same StateGraph interface.
Intelligent Agent Orchestration
At the core of every Agentica deployment is an intelligent agent loop. The agent reasons about your request, decides whether to call external tools, processes results, and continues until it has a complete answer.
The core agent implements a ReAct (Reason + Act) pattern as a 2-node StateGraph: ENTRY → chat_node → (tool_calls present?) → tool_call_node → chat_node → ... → END. Conversation state is checkpointed after every node execution via AsyncPostgresSaver backed by a psycopg3 AsyncConnectionPool, enabling multi-turn conversations with full state continuity, session resumption, and full state replay for debugging.
LLM Resilience and Fault Tolerance
AI models can fail — rate limits, timeouts, provider outages. Agentica maintains a registry of multiple LLM models and automatically rotates between them if one fails.
The LLMService maintains an LLMRegistry of 5 pre-initialized model instances. When a call fails, tenacity retry logic attempts the same model up to 3 times with exponential backoff (minimum 2s, maximum 10s wait). If all retries are exhausted, the service performs a circular model fallback. Stress-tested with 1,500 concurrent users: 98.4% success rate, 1.2s average latency.
Long-Term Memory Across Sessions
Agentica remembers. Every interaction contributes to a persistent memory layer that spans sessions, users, and time.
Long-term memory is implemented via mem0ai AsyncMemory with a pgvector backend for vector similarity search. Memories are injected into the system prompt as context. After response generation, memory updates run as a non-blocking background task via asyncio.create_task(), ensuring memory persistence never adds latency.
Real-Time Streaming
For interactive applications, Agentica delivers responses character-by-character via real-time streaming. Users see the AI thinking in real time.
Streaming is implemented via Server-Sent Events (SSE) using LangGraph’s astream() method with stream_mode='messages'. Each token arrives wrapped in SSE format with a structured StreamResponse schema. Error handling is granular — if the stream crashes mid-way, the system sends an error via the SSE channel rather than silently dropping.
Continuous Quality Monitoring
Every response Agentica generates is automatically scored across five quality dimensions: factual accuracy, helpfulness, relevancy, conciseness, and safety.
The evaluation framework runs as an offline pipeline processing traces from the Langfuse observability platform. Each metric uses a dedicated prompt template and produces structured scores via LLM-as-a-Judge methodology. Prometheus collects real-time operational metrics — HTTP request counts, LLM inference latency, streaming duration, and database connection gauges.
Why Agentica is Different
17 Architectures, Not One
Most platforms give you a chatbot. We give you 17 composable reasoning patterns — the right tool for every problem, not a one-size-fits-all approach.
Safety Built In, Not Bolted On
Four of our 17 architectures are dedicated safety patterns: human approval gates, risk simulation, self-healing pipelines, and agents that know their own limits.
Memory That Persists
Your AI remembers. Cross-session memory means every interaction builds on the last — no more explaining context from scratch.
Self-Healing by Design
Automatic retries, model fallback across 5 LLMs, graceful degradation. Your AI stays online even when providers go down.
Observable, Always
Every request traced. Every response scored. Prometheus metrics, Grafana dashboards, and Langfuse traces from day one.
See the Architecture in Action
Write to us. We will walk through the architecture that fits your use case — from request to response.