The evolution from traditional AI to agentic AI is more than a shift in terminology- it is an architectural transformation. Unlike LLMs that remain stateless and reactive, agentic systems plan, act, and adapt to dynamic environments. This requires a stack of technical components working in tandem. In this article, let’s dissect the internals of agentic AI, focusing exclusively on its technical design patterns.
1. Multi-Agent Orchestration
At runtime, an agentic system resembles a distributed execution engine. Each agent is an autonomous process with a policy function that determines actions based on state and context.
- Planner: Implements task decomposition using reasoning frameworks (e.g., ReAct, Tree-of-Thoughts). The planner generates directed acyclic graphs (DAGs) of subtasks.
- Executor Agents: Specialized units interfacing with external systems. They expose an action schema (OpenAPI specs, RPA workflows, or gRPC endpoints) and accept structured commands.
- Critic/Verifier: Implements programmatic validators. For example, it may run SQL assertions, schema checks, or domain-specific rules against executor outputs.
- Orchestrator: A central controller that manages agent lifecycles, ensures idempotency, and performs failure recovery. Orchestrators often run as microservices, with Redis/Kafka backplanes to handle messaging.
This orchestration layer is analogous to BPMN engines, but instead of hard-coded workflows, planning and routing are dynamically generated at runtime.
2. Planning Algorithms
The technical backbone of agentic AI is its ability to create execution plans dynamically. Current implementations rely on:
- ReAct (Reason + Act): Alternates between reasoning steps (natural language reasoning traces) and action steps (API invocations).
- Tree-of-Thoughts (ToT): Expands multiple reasoning paths in parallel, scoring branches with heuristics, and pruning suboptimal plans.
- Graph-based Planners: Build DAGs of tasks where dependencies are explicitly represented. These DAGs can be optimized using topological sort or constraint-satisfaction solvers.
- Symbolic + Neural Hybrid: LLM proposes a plan; a symbolic executor (Prolog-like logic engine) validates constraints before execution.
From an engineering lens, these planners run within a sandboxed interpreter, where the LLM’s natural-language reasoning is parsed into structured command graphs.
3. Memory Architecture
A critical differentiator in agentic systems is persistent and retrievable memory. Architecturally, memory is layered:
- Ephemeral (short-term): Stored in in-process buffers (e.g., JSON objects, Redis cache), it is useful for a single workflow run.
- Persistent (long-term): Implemented via vector databases such as Pinecone, Weaviate, or FAISS. Input/output embeddings are indexed for semantic retrieval.
- Episodic Memory: Stores execution traces (plans, actions, outcomes). This is serialized in event logs and replayed for debugging or fine-tuning.
- Declarative Memory (Knowledge Graphs): Entity-relationship models (Neo4j, RDF triples) allow agents to reason over structured enterprise knowledge.
Memory lookups typically follow a retrieval-augmented generation (RAG) pipeline: embeddings → similarity search → context injection into prompts. Advanced setups use hierarchical memory, prioritizing recent episodic traces over historical embeddings.
4. Tool Invocation Layer
Agents need deterministic ways to call external systems. Architecturally, this requires a tool invocation layer:
- Schema Registry: Defines available tools and their input/output schemas (JSON Schema, Protocol Buffers).
- Function Calling Interface: LLM outputs JSON-like action specifications validated against schemas. Example: { “action”: “CreateInvoice”, “params”: {“customerId”: 1234, “amount”: 2500} }
- Execution Middleware: Maps validated requests to actual connectors (REST, SOAP, RPA workflows, gRPC, or message queues).
- Sandboxing: Tools run in controlled environments with guardrails, time-outs, circuit breakers, and retries.
This design ensures that agent outputs are machine-interpretable instructions, not free text, reducing hallucinations and runtime errors.
5. Feedback and Self-Correction
An agentic system cannot assume success-it must evaluate. Feedback loops operate at three levels:
- Syntactic Validation: Schema validation, type checks, and contract enforcement.
- Semantic Validation: Domain-specific assertions (e.g., “invoice total matches line-item sum”).
- Policy Enforcement: Governance rules like RBAC, compliance constraints, or budget limits.
If validation fails, the orchestrator triggers a self-correction cycle:
1. Critic Agent generates error diagnostics.
2. Planner reworks the execution graph with adjusted constraints.
3. Executor retries only failed nodes (idempotent design).
This enables fault-tolerant execution similar to checkpointing in distributed systems.
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6. Execution Flow Example
Consider a procurement workflow:
1. Trigger: “Onboard vendor and process first invoice.”
2. Planner: Decomposes into subtasks → KYC validation → Vendor record creation (ERP) → Invoice ingestion → Payment scheduling.
3. Memory Lookup: Retrieves past onboarding flows and compliance rules.
4. Tool Invocation:
- KYC API → validates documents.
- ERP connector → inserts vendor record.
- RPA bot → enters invoice into legacy system.
5. Critic: Validates ERP response (vendor ID created, no duplicate).
6. Feedback Loop: Logs execution trace in episodic memory for future reuse.
The orchestration engine ensures partial failures (e.g., ERP outage) are retried with backoff, while the planner dynamically reorders tasks if dependencies shift.
7. Runtime Infrastructure
To scale in enterprise environments, agentic systems are deployed as distributed services:
- Containerized Agents: Each agent packaged as a microservice (Docker + Kubernetes).
- Messaging Backbone: Kafka/RabbitMQ for inter-agent communication.
- Vector Stores: Deployed alongside GPU-serving clusters for low-latency retrieval.
- Observability: Traces logged via OpenTelemetry; Grafana dashboards monitor task execution rates, failures, and retries.
- Security Layer: API gateways enforce OAuth2, rate limits, and data masking before tools are invoked.
This runtime view aligns agentic AI with existing enterprise DevSecOps pipelines.
8. Design Considerations
When architecting agentic AI, engineers must account for:
- Determinism vs. Stochasticity: Introduce guardrails so probabilistic outputs don’t break critical workflows.
- Explainability: Persist reasoning traces for auditability (critical in finance, healthcare).
- Cost Optimization: Hybrid models-lightweight local models for routine reasoning, large hosted LLMs for complex planning.
- Isolation: Sandbox risky actions (file system writes, network calls) to prevent cascading failures.
Powering the Next Wave of Intelligent Systems
By integrating planning, autonomous reasoning, and adaptive learning, agentic systems unlock capabilities far beyond the reactive nature of LLMs. As enterprises embrace these design patterns, agentic AI will pave the way for smarter, context-aware, and self-improving applications that redefine how businesses operate and innovate.
