GenAI Agents: Understanding the Technology Stack

AI
1

Why “agents” are not a single technology — and what actually makes them work in enterprise

Last week, I attended the AWS Summit, where discussions prominently featured GenAI agents. This prompted me to take a step back and systematically consider the essential requirements for implementing enterprise-grade agent solutions, which I want to share in this article. Crucially, an “agent” should not be viewed as a single product or framework; rather, it represents a composite of multiple technologies.

At Pega, we are dedicated to developing comprehensive solutions; however, it remains vital to understand the distinct components that constitute an enterprise agent, independent of platform choice. Familiarity with these elements is important within Pega as well, since additional configuration may be necessary to meet specific organizational demands.

Even utilizing Pega, organizations may need to establish a broader agent landscape, positioning Pega as one element within a larger enterprise ecosystem. This approach necessitates the integration of Pega with various systems and technologies to create a robust and effective agent infrastructure.

Looking ahead, consider a future in which agents manage all operational tasks. How might such a goal be realized? My analysis indicates this outcome is already largely feasible:

1. The probability of success is influenced by numerous factors—organizational, legal, and cultural. This article will specifically examine the technological dimension.

2. Implementation efforts: Pega offers out-of-the-box GenAI capabilities with streamlined, prescriptive agentic deployment. Alternative platforms typically require assembly of the full technology stack (refer to the table below for several examples from my research).

3. Timeframe: With Pega, organizations can deliver initial GenAI capabilities, or even deploy the first agent, within approximately one week. While timeframes vary across platforms, the recommended approach is to think strategically but begin with small, manageable steps—regardless of platform selection.

Let’s now dive into a magnificent world of GenAI Agents technology stack!

The Base Agent Model

At a minimum, a production‑ready agent consists of the following building blocks:

  1. Model (LLM / VLM / multimodal)

  2. Orchestration / Agent runtime (planning, agent loop)

  3. Instructions / Prompting / Policies

  4. Memory

    • Short‑term (context window)

    • Long‑term (vector DB, knowledge base)

  5. Tools + tool execution layer

  6. Access control & security (auth, guardrails, sandboxing)

  7. Runtime / infrastructure

  8. Observability & governance (evaluation, tracing) ← often underestimated

Technology Components and Capabilities in Agent Architecture

The table below outlines how various popular technologies address aspects of GenAI Agents architecture based on my experience with custom agent development. Your views may differ - feel free to comment below.

genai matrix
genai matrix1312×800 188 KB

GenAI Agents components details

Model layer (reasoning & generation)

  • OpenAI (GPT)

  • Anthropic (Claude)

  • Google (Gemini)

  • Amazon Bedrock (abstraction across models)

  • Pega (model‑agnostic, via managed providers)

Key insight:
:backhand_index_pointing_right: Models today are largely an interchangeable commodity when accessed through abstraction layers (e.g. Bedrock, LiteLLM, Pega GenAI services).

Orchestration / Agent Runtime (where agent behavior emerges)

Responsible for the loop:
think → decide → act → observe → repeat

Common approaches:

  • LangChain (chains + agents)

  • LangGraph (state machines — critical for production)

  • CrewAI (multi‑agent collaboration)

  • Lightweight orchestrators (Strands, OpenClaw)

Pega angle:

  • Pega uses proprietary implementation for an agent loop

  • Pega provides similar orchestration role to frameworks like LangGraph, but at enterprise scale

  • Governance baked into execution, not bolted on later

Key insight:
:backhand_index_pointing_right: The “magic” of an agent is here — not in the model.

Instructions, Prompting & Policies

Includes:

  • system prompts

  • role definitions

  • constraints

  • business and compliance policies

Typical implementations:

  • Prompt templates (everywhere)

  • Role‑based agents (CrewAI)

  • Structured instructions and outputs (OpenAI Assistants, LangChain)

Pega angle:
Prompts are not free text artifacts — they are:

  • versioned

  • configurable

  • executable within governed flows

  • auditable (who changed what, and when)

Key insight:
:backhand_index_pointing_right: An “agent” is largely prompt engineering + controlled execution.

Memory (the production bottleneck)

Types:

  • Short‑term memory: context window

  • Long‑term memory: vector DB, knowledge store

Implementations:

  • LangChain memory modules

  • LangGraph state persistence

  • OpenAI threads

  • Bedrock via AWS services (S3, Kendra, OpenSearch)

Pega angle:
Memory becomes more reliable when:

  • tied to cases and business entities

  • scoped by access control

  • governed by data retention rules

Key insight:
:backhand_index_pointing_right: Memory is the hardest part of production agents, not reasoning.

This is because implementing effective memory in production agents involves complex challenges such as reliably storing and retrieving information over time, maintaining context across sessions, and ensuring data security and compliance. Unlike reasoning, which can often be handled with advanced models and algorithms, memory requires integration with databases or knowledge stores, careful management of access controls, and adherence to data retention policies. Additionally, in enterprise scenarios like Pega, memory must be tightly linked to cases and business entities, scoped by user permissions, and governed by organizational rules, making it a multifaceted technical and governance challenge.

Tools (what makes an agent useful)

Tools include:

  • API calls

  • database queries

  • case actions

  • code execution

Common patterns:

  • OpenAI function calling

  • LangChain tool abstraction

  • CrewAI shared tools

  • Bedrock tools via AWS services

Pega angle:
Agents can use tools based on:

  • Case Types
  • API
  • Data pages
  • RPA
  • Activities

Key insight:
:backhand_index_pointing_right: An agent without tools is just a chatbot. And Pega currently is one of the best platforms for GenAI Agents tools availability and governance.

Security & Guardrails

Covers:

  • identity & access

  • prompt injection protection

  • sandboxed execution

Reality check:

  • managed platforms handle this better

  • open‑source frameworks often leave gaps

Pega angle:
Security is inherited from:

  • role‑based access / attribute-based access

  • decision‑driven authorization

  • environment isolation

  • audit

Key insight:
:backhand_index_pointing_right: Security is a weak spot for many DIY stacks.

Runtime & Infrastructure

Includes:

  • where the agent runs

  • scaling

  • async execution

Pega angle:
Runtime is:

  • managed

  • scalable

  • environment‑aware

  • already enterprise‑certified

This is Pega-as-a-Service for you!

Key insight:

:backhand_index_pointing_right: Most frameworks assume you will solve runtime separately

Observability, Evaluation & Governance

Often forgotten — always painful later:

  • tracing

  • debugging

  • response quality

  • agent drift

Tools:

  • LangSmith

  • Helicone

  • CloudWatch

Pega angle:
Observability is embedded into:

  • Case Audit log

  • PDC Alerts

  • Agent explainability

Key insight:
:backhand_index_pointing_right: Production agents without observability do not scale.

A Simple Mental Model: 3 Layers

If you are just starting this number of components to worry about may feel daunting. Here is the simple 3-layer approach that will help you structure your efforts and do not forget anything.

1. Intelligence layer

  • foundation models (GPT, Claude, Gemini, etc.)

2. Control layer

  • orchestration

  • prompting & policies

  • memory

3. Execution layer

  • tools

  • runtime

  • security

  • observability

This explains why enterprise platforms like Pega with process, rules, and governance at their core adapt very naturally to agentic patterns.

Final Thought

If you want agents to actually work:

  • start small

  • design for control, not just creativity

  • treat observability and security as first‑order concerns

Agents are not magic, but when built on a well-designed stack, their abilities are truly incredible. They can automate complex workflows, respond dynamically to evolving business needs, and drive measurable value for organizations. Reflecting this spirit of innovation, Dr. Werner Vogels, in his closing keynote at the AWS Summit, challenged attendees to embrace experimentation and creativity: “Now Go and Build Something!”—a call to leverage technology for meaningful impact and continuous improvement.