Agent types in production

Our agent ecosystem divides work into two clean classes: Reasoning Agents that think in real time, and Operational Agents that reliably execute repetitive workflows with speed, safeguards, and predictable cost.

Reasoning Agents

Reasoning Agents use high-capacity models (e.g., GPT-5, Claude Opus, Gemini Pro, Meta Llama) to plan, analyze, and decide in interactive contexts, selecting tools, reconciling evidence, and producing business-grade outputs for complex tasks that benefit from judgment.

Deep Research Agents

Proactively investigate topics across sources, synthesize findings, resolve contradictions, and propose next steps with citations and confidence measures.

e.g. Investigates key accounts, compares filings and news, and drafts a C-suite briefing with cited risks and opportunities.

Customer Case Study

Customer: A large global bank
Problem Statement: The client teams often meet their customers with very little information about what's happening out in the market. This results in mismatched value prop selling.

Agentic AI Solution on Lyzr: A Multi-Agent System Analyzer, a multi-agent system that not only picks client data from the internal databases but also researches about the client on the internet, combines both the data to give one holistic view of where the client is and what are the new services that could be sold.

Negotiation Agents

Model counterpart interests, propose trade-offs, and draft language that aligns terms, policy guardrails, and acceptable ranges during structured negotiations.

e.g. Suggests alternative indemnity wording to meet policy, offers pricing concessions, and compiles a redlined contract for review.

Customer Case Study

Customer: 60 Hertz, Publicis

Knowledge Search Agents

Query knowledge bases and external sources, rank results by relevance and trust, and deliver grounded answers with links.

e.g. Searches product wikis and tickets, explains behavior, and links directly to the canonical troubleshooting runbook steps.

Customer Case Study

Customer: HFS Research - Ask HFS AI
Problem Statement: HFS Research's traditional search system couldn't interpret query intent or synthesize insights from 4,000+ research assets, with inconsistent metadata making filtering unreliable and inability to handle varied query types.

Agentic AI Solution on Lyzr: Lyzr built a knowledge-graph along with a multi-agent architecture and layered retrieval combining freshness scoring with semantic matching for precise, cited responses.

Conversational Agents

Hold stateful dialogues, clarify intent, and orchestrate tools while keeping responses on-brand, compliant, and contextually aware.

e.g. Guides a customer through identity verification, checks order status, and initiates a return with shipping label creation.

Customer Case Study

Customer: Saksoft HR Helpdesk

Content Generation Agents

Draft long-form or channel-specific content, adapt tone, and embed structured facts while respecting brand and legal guidance.

e.g. Produces a partner announcement, localizes copy, inserts approved claims, and generates alt-text plus meta descriptions.

Customer Case Study

Customer: Hitachi - Ebook Writing Agent

Data Analysis Agents

Explore datasets, build queries, run statistical tests, and narrate insights with clear caveats, visuals, and decision recommendations.

e.g. Analyzes churn cohort data, fits retention curves, and explains drivers with SQL snippets and chart annotations.

Customer Case Study

Customer: 60 Hertz, Publicis

Evaluation Agents

Score outputs against policies, rubrics, or metrics, provide critiques, and recommend targeted fixes to improve quality and safety.

e.g. Reviews marketing claims against legal checklist, flags risky phrases, and proposes compliant rewrites with rationale.

Customer Case Study

Customer: Accenture Spotlight Agent

Document Analysis Agents

Parse lengthy documents, extract entities, compare sections, and summarize implications with citations and suggested owner actions.

e.g. Compares MSA versus SOW, highlights conflicting termination terms, and lists remediation options for procurement sign-off.

Customer Case Study

Customer: SMBC Customer Onboarding Agent