Introduction

Welcome to the Agent Handbook — a guide for technical leaders navigating the next phase of enterprise AI.

If you’re here, you’re likely facing a familiar situation:

• Your team has successfully built a small proof of concept using OpenAI or LangChain
• The results are promising — summarization, ticket tagging, workflow enhancement
• But you’re starting to ask hard questions:
  • Is this reliable enough to scale?
  • How do we measure or improve performance over time?
  • What happens when we need to support 10x the volume, or 10x the use cases?

This handbook is written for you — the CTO or technical decision-maker — tasked with not just building a prototype, but delivering a repeatable, secure, and scalable AI capability for your organization.

🧠 First Steps Beyond Prompting

This handbook will help you move from single-shot prompting to structured, composable intelligence. An AI-enabled, production-ready system will allow you to:

• Break down complex tasks into smaller, reusable components
• Chain steps together into workflows
• Add reasoning, tools, memory, and state
• Reuse logic across use cases

This is how we go from one-off wins to enterprise-wide transformation.

But here’s the hard truth: while it’s easy to build an agent that works once — it’s incredibly difficult to build one that works reliably, at scale, with visibility, evaluation, and cost control.

🔀 The Build vs Buy Dilemma

Many teams start with raw APIs or open frameworks like LangChain or Autogen. It’s a great way to learn. But scaling these homegrown systems introduces a second set of problems:

• Prompts live in code → hard to upgrade or evaluate
• No cost or performance visibility
• No way to measure regressions
• Security concerns when working with private data
• Workflow complexity increases fast — retries, branching, context windows, external tools

These are not LLM problems. They’re software engineering problems.

You could build your own internal stack to handle them. Or you could use one that was purpose-built for this exact need: The Aegis Stack.

🎯 What Aegis Is — and Isn’t

Aegis is purpose-built to automate repetitive, text-driven manual processes.

If your organization is buried under unstructured data — emails, forms, tickets, PDFs — and you have real bottlenecks in operations, support, or analysis that rely on human review, Aegis can help.

We help:

• Extract and structure messy internal data
• Run repeatable agent workflows on top of it
• Measure quality and cost over time
• Build internal capability around agent design and evaluation

Aegis is not:

• A “chat with your docs” tool
• A generic chatbot framework
• A black box automation platform

We’re not trying to replace people with bots. We’re helping teams eliminate the manual, error-prone glue work that slows them down — with modular, testable, and transparent agent systems.

🎯 Real-World Problems Aegis Solves

🧑‍💼 LMS CTO

“We’re onboarding more institutions every month, but we keep hearing the same complaint: ‘We don’t have enough assessors.’ Auto-grading multiple choice is easy — but it’s the free-text responses that bottleneck everything. Our platform captures a lot of metadata, but we don’t have a way to triage or evaluate subjective answers without involving a human.”

With Aegis, you can triage submissions, flag poor-quality answers, auto-draft feedback, and track model performance over time — giving educators tools, not black boxes.

⚖️ LegalTech CTO

“Every law firm and legal department we work with wants faster contract review — but with consistency and traceability. We’ve tried simple regex or ML, but they fail on edge cases. Our users still manually flag risk clauses, check indemnity language, and summarize deviations — it’s slow and inconsistent.”

Aegis lets you build agent workflows that handle clause tagging, redline suggestions, and automated summaries — while surfacing uncertain outputs for human-in-the-loop validation.

🏭 Manufacturing CTO

“Every RFP we respond to is slightly different, and yet our engineers keep rewriting the same product descriptions and scanning specs manually. Our pre-sales team is swamped. We also waste time chasing compliance wording across safety, warranty, and QA docs.”

With Aegis, you can parse and structure inbound RFPs, reuse approved blocks of text, flag gaps, and auto-fill compliance sections — cutting days off the tender cycle.

💼 HR Tech CTO

“Customers love that we streamline hiring, but we still rely on human assessors to review written responses or interview feedback. Startups ask if we can help rank candidates or suggest next steps. We have the data, but no process to make it actionable.”

Aegis can power internal agents to analyze feedback, summarize interviewer notes, score responses, and suggest follow-up questions — turning chaos into a structured funnel.

🧑‍💻 Enterprise IT / Support CTO

“We’re flooded with support tickets — 70% are repetitive, but 30% are business-critical. Our customers want faster triage, better routing, and fewer dead ends. Our team is struggling to maintain dozens of intent models and workflows.”

Aegis gives you flexible, prompt-driven agents that can classify, tag, escalate, or even auto-respond to common cases — with cost tracking and performance evaluation built in.

🚀 What This Handbook Offers

This handbook is structured around the three stages of every LLM automation journey:

1. Understand — What agents are, why prompting alone doesn’t scale, and why stochastic systems need new thinking.
2. Build — How to design and evaluate agents like software systems, using prompts, RAG, workflows, and evaluation pipelines.
3. Scale — How to get cost visibility, security, feedback loops, and orchestrated automation across your organization.

Along the way, we’ll introduce you to the design patterns and components of the Aegis Stack — and help you decide which pieces make sense to adopt, and when.

This isn’t just about building agents. It’s about making enterprise AI a repeatable, trusted capability in your organization.

Let’s begin.