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Xfactr.ai

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AI & Agentic AI

Not automation.
Agency.

There is a difference between a system that follows instructions and one that pursues goals. We build the latter.

Talk to Our Team
GOAL
IN
Decompose
sub-tasks
Plan Steps
sequence
Select Tool
api • db • search
Execute
tool_call()
Observe
result • error
Adapt
replan
GOAL
MET
01 • receive
02 • think
03 • plan
04 • choose
05 • act
06 • see
07 • learn
08 • done
← agent loops until goal is met or confidence falls below escalation threshold →

automation spectrum

From automation to agency
five levels of AI capability.

LEVEL 01
Rule-Based Automation
Scripted logic. Deterministic. Breaks on unexpected input. No intelligence structured repetition.
if/else • RPA scripts • batch jobs
LEVEL 02
AI-Assisted
Model makes suggestions. Human decides and acts. Copilot patterns reduce effort per task.
GitHub Copilot • AI drafts • smart suggestions
LEVEL 03
AI-Directed
AI makes decisions within defined scope. High-confidence actions execute automatically.
AI triage • decision engines • auto-classification
LEVEL 04 ✦
AI Agents
Goal-directed planning, tool use and adaptive execution with escalation at confidence boundaries.
LangGraph • AutoGen • task completion
LEVEL 05 ✦✦
Multi-Agent Systems
Networks of specialised agents coordinated by an orchestrator for complex workflows.
CrewAI • orchestrator • parallel execution

How agents reason

The ReAct loop the core pattern behind every agent we build.

THOUGHT
What do I know?
What do I need?
What should I do next?
→ LLM call with
context + history
+ tool descriptions
tool_call
ACTION
search(query)
db.query(sql)
api.post(payload)
file.write(output)
result
OBSERVE
Was the action successful?
Did the result match intent?
Is the goal closer to met?
Observation stored in agent memory.
✓ Goal Met
⚑ Escalate
↻ Max Steps
← agent loops until: goal_met OR confidence < threshold OR steps == max_steps →

Multi-agent architecture

How specialist agents collaborate under an orchestrator.

One orchestrator. Multiple specialists. Each agent handles what it’s good at. The orchestrator manages sequencing, state, conflict resolution, and the human handoff.

USER GOAL "Analyse Q3 contracts & flag risk" ORCHESTRATOR Decomposes goal Routes sub-tasks Synthesises outputs shared state store · checkpointing LangGraph state graph · Redis · Postgres ✦ research-agent RAG · web · DB search → grounded context retrieved ✦ decision-agent Scoring · classification → risk scores per clause ✦ doc-intel-agent Contract extraction → entities + obligations ✦ synthesis-agent Report generation → final risk brief RESULT Structured risk report Clause-level citations Confidence per finding HUMAN ✓

Agent capability types

Categories of AI agents we build and deploy.

Category 01
Research & Retrieval Agents
Agents that search, retrieve, synthesise and summarise information from databases, documents, web and APIs.
Can Do
  • Retrieve documents across vector stores and structured DBs
  • Synthesise findings from multiple sources
  • Verify claims and flag conflicts
  • Maintain citation chains
Category 02
Decision & Scoring Agents
Evaluate requests, score options, route decisions and automate approval or classification tasks.
Can Do
  • Score using multi-criteria rubrics
  • Route with audit trail
  • Explain decisions
  • Escalate below-threshold cases
Category 03
Document Intelligence Agents
Extract structured information from contracts, invoices, reports and emails for downstream systems.
Can Do
  • Extract entities & relationships
  • Handle layout variation
  • Validate business rules
  • Self-correct low confidence
Category 04
Workflow Orchestration Agents
Coordinate multi-step business workflows, manage state, retries and system integrations.
Can Do
  • Maintain workflow state
  • Retry failed steps
  • Coordinate multiple APIs
  • Produce status summaries
Category 05
Monitoring & Exception Agents
Monitor telemetry, detect anomalies, automate remediation and escalate safely.
Can Do
  • Correlate logs & metrics
  • Run remediation playbooks
  • Generate diagnostics
  • Learn from incidents
Category 06
Multi-Agent Coordinator
Orchestrate specialist agents, merge outputs and deliver coherent final results.
Can Do
  • Decompose complex goals
  • Route to specialist agents
  • Merge outputs
  • Manage dependencies

AI foundation

What sits beneath every agent and AI system we build.

Layer 01 · Models
Foundation Models
OpenAI GPT-4o Anthropic Claude 3.5/4 Google Gemini 2.0 Meta Llama 3.3 Mistral Large AWS Bedrock Azure OpenAI Vertex AI Cohere Command R+ Fine-tuned Domain Models
Layer 02 · Retrieval
RAG & Knowledge
LlamaIndex LangChain RAG Pinecone Weaviate Qdrant pgvector ChromaDB Azure AI Search OpenSearch Semantic Chunking HyDE Retrieval Rerankers
Layer 03 · Agents
Agent Frameworks
LangGraph AutoGen CrewAI LangChain Agents Semantic Kernel Haystack Custom ReAct Loops OpenAI Assistants API Anthropic Tool Use
Layer 04 · Memory
State & Memory
LangGraph Checkpointing Redis Conversation Store PostgreSQL State Zep Memory MemGPT Short-term (In-context) Episodic Memory Semantic Memory
Layer 05 · Observe
Observability & Evals
LangSmith MLflow Weights & Biases Evidently AI Arize AI Helicone Custom Eval Harnesses RAGAS (RAG Eval) OpenTelemetry
Layer 06 · Safety
Guardrails & Trust
NeMo Guardrails Guardrails AI Custom Safety Classifiers Bounded Tool Permissions Confidence Thresholds Human-in-the-loop Gates Audit Logging Action Reversibility Checks

XFACTR.AI PLATFORM

Makez.AI - where AI applications are assembled, not just described.

// Makez.AI is our platform for building, testing, and deploying // AI-powered applications and agent workflows from prototype // to production-grade enterprise deployment.

Visual Agent Builder

Design multi-agent workflows visually connect agents, define tool access, set escalation thresholds without writing orchestration code from scratch.

Evaluation-First Development

Every agent workflow ships with an eval harness. Test against ground-truth examples before changing a prompt or swapping a model.

Production-Ready Deployment

From prompt playground to Kubernetes-deployed, monitored, versioned AI service with full observability built in, not bolted on.

Explore Makez.AI ↗ Request a Demo
makez.ai · Agent Workflow Builder
user_input trigger orchestrator GPT-4o LangGraph research RAG · search decision scoring synthesis output gen result ✦ citations
eval harness · 94% pass · latency p95: 3.2s · token cost: $0.004/run

Where these capabilities apply

AI & Agentic AI across our service areas.

SERVICE ↗
AI Applications
End-to-end AI-native software including GenAI, vision, predictive analytics, and intelligent agent systems.
→ ai-applications
SERVICE ↗
Process Automation
Agentic workflows replacing manual processes from IPA and IDP to autonomous multi-agent operations.
→ process-automation
SERVICE ↗
Application Management
AI operations with predictive monitoring, anomaly detection, self-healing automation, and incident management.
→ application-management
SERVICE ↗
Managed Support
LLM-powered ticket triage, AI resolution suggestions, and intelligent escalation workflows.
→ managed-support
SERVICE ↗
Smart Enterprise Solutions
Decision engines, RAG copilots, embedded AI, and enterprise agent workflows built into core systems.
→ smart-enterprise-solutions
SERVICE ↗
DevOps & MLOps
CI/CD, model versioning, monitoring, deployment automation, and production-ready AI infrastructure.
→ devops-mlops
SERVICE ↗
Data & Analytics
Feature stores, RAG pipelines, vector databases, analytics platforms, and AI-ready data lakes.
→ data-analytics
PLATFORM ↗
Makez.AI
A unified platform to build, orchestrate, evaluate, and deploy AI agents at production scale.
→ makez.ai

Connect With us

What would you give an agent to own?

Describe the process the goal, the tools it would need, the decisions it should make vs escalate. We’ll design the agent architecture from there.

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