When one agent isn't enough, orchestrate many.

Agentic AI is a system of specialist agents coordinated by an orchestrator that manages state, handles failures, and pursues complex goals autonomously. Built for workflows that a single agent cannot handle — legally, financially, or operationally.

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40+

AI Systems In Production

Years Building Langgraph

ISO 9001 & 27001

Certified

Clutch

Trusted by

Agentic AI systems we architect and deploy

Six production system types — each requiring multi-agent orchestration that a single agent cannot deliver.

Legal & Compliance Review

Contract review orchestrator routes to: jurisdiction agent, risk flagging agent, precedent search agent, and summary agent. Produces structured legal analysis at a fraction of the time and cost of manual review. Human review for exceptions and high-risk clauses.

Financial Analysis Pipelines

Orchestrator delegates to data retrieval, quantitative analysis, narrative synthesis, and validation agents. Institutional-grade research reports produced autonomously.

Software Engineering Agents

Development orchestrator coordinates code generation, test writing, code review, documentation, and PR management agents. Accelerates well-defined delivery tasks.

Customer Intelligence Systems

Agents analyse tickets, query CRM, assess usage patterns, identify churn signals, and generate retention actions — continuously across your entire customer base.

Supply Chain Intelligence

Multiple agents monitoring inventory, supplier lead times, logistics, demand forecasts, and exception queues. Correlates signals and acts before problems materialise.

Research Automation

Agents search web and academic sources, extract from documents, synthesise findings, identify contradictions, and produce cited structured reports. Days of research in minutes.

Who hires us for Agentic AI

CTOs with multi-step workflows that defeat single agents

The task is too complex for a single context window, requires parallel processing across specialist domains, or gates on intermediate outputs that need validation before proceeding. You need orchestration — not a bigger prompt.

Legal & compliance teams drowning in document volume

High contract or document volume, consistent analysis framework required, regulatory or audit trail obligations. Agentic AI delivers consistency and speed that humans cannot match at scale.

Enterprise product teams building AI-native features

Your product's differentiation is an autonomous, multi-step AI capability. You need engineers who've built production agentic systems — not a team discovering LangGraph's failure modes on your roadmap.

Operations leaders with high-complexity, high-volume processes

Supply chain correlation, financial report generation, customer intelligence at scale. These tasks require specialist knowledge across multiple domains — exactly what multi-agent architecture delivers.

LangGraph expertise.Evaluation-first.Honest about complexity.

Four things that separate an EB Pearls agentic system from an orchestration demo that degrades in production.

LangGraph expertise — not just LangChain

Reliable agentic systems need LangGraph — with state, cycles, and conditional logic. We’ve built with it since early availability and know how it performs at scale.

Evaluation framework before the build

We create evaluation datasets, scoring, and regression testing upfront — so you know the system is improving.

We'll tell you when it's overkill

Your product's differentiation is an autonomous, multi-step AI capability. You need engineers who've built production agentic systems.

Production operations from day one

We monitor, alert, and improve systems continuously to handle drift and scale.

Already using an AI agent?Here's when to go further.

Knowing the boundary saves you from under-building or over-engineering.

Capability needed	Single AI Agent	Agentic AI System (EB Pearls)
Task fits one context window	✓ Single agent handles it	✓ Also handles it — but overkill
Requires 3+ specialist domains	✗ One LLM generalises poorly	✓ Specialist sub-agent per domain
Parallel processing required	✗ Sequential only	✓ Orchestrator dispatches in parallel
Intermediate output gates next step	✗ No state between steps	✓ LangGraph state machine manages dependencies
Best model per task required	✗ One model for everything	✓ Best model per sub-agent task
Time to first value	6–10 weeks	10–24 weeks depending on complexity

Our agentic AI stack

★ marks our preferred production choice for Australian enterprise deployments.

Orchestration

★ LangGraph
★ LangChain
LlamaIndex
AutoGen (Microsoft)
AWS Step Functions

Model Layer

★ AWS Bedrock
★ Claude 3.5 Sonnet
OpenAI o1 / o3
Fine-tuned (SageMaker)
Llama 3 on-premise

State & Memory

★ LangGraph state machines
★ DynamoDB
Redis (ephemeral)
pgvector / Pinecone
AWS OpenSearch

Eval & Observability

★ LangSmith
★ Weights & Biases
Custom eval harnesses
AWS CloudWatch
Datadog

Real projects.Measurable outcomes.

Two recent multi-agent deployments. Numbers from the first 90 days in production.

During the entire project, what I witnessed was an unwavering determination to keep forging ahead and readily adapting to every new challenge that came our way. The performance was outstanding, and we owe our success to you. Without your contributions, this wouldn’t have been possible. Great job!

Alison Barandon

Marketing Manager at Mondial VGL

We finished our MVP on time and on budget and are on our way to building a strong business. Thanks to the guys at EB Pearls, they pay a lot of attention to our projects and help ensure our success.

Mitchell Stapleton-Coory

Founder, Bellr

The client is delighted with EB Pearls’ ability to deliver within the established deadlines through a professional approach. They use various communication tools to ensure a solid project management experience. The team also praises their overall good service and constant commitment to learning new things.

John Walton

EB Pearls do high-quality work in the allotted timeframe. They maintain clear and consistent communication, providing updates on key project components and listening to feedback. There are numerous different stages, processes, and statuses in our project which made the work very complicated, but EB Pearls worked it all out.

Blake Weston

Find Fill Storage

Throughout the app development process the developers ensure their team work closely with us and also ensure the app meet our needs and aligns with our business goals. They also manage effective communication, collaboration and project management. The app is essential and was also delivered on time within budget. We are extremely satisfied.

Hamid Moghadam

EB Pearls provided great clarity on the scope, timeline, design, and stages of developing our app, which gives users access to a database of more than 1,000 roles. They stuck rigidly to our production schedule and delivered as promised.

Mike Fields

Director, Care Careers

Throughout the app development process, the development team works closely with us and also ensure that the app meets our needs and aligns with our business goals. They also manage effective communication, collaboration, and project management. The app is essential and was also delivered on time, within budget. We were extremely satisfied with their work.

Brett Thome

From design to production

Stage 01

System Design & Scoping

Weeks 1–3

Map the full workflow. Define agent boundaries. Design the evaluation framework. Architecture document with fixed-price quote.

Stage 02

Prototype & Evaluation

Weeks 4–6

Build the orchestration skeleton. Create evaluation dataset. Establish baseline metrics. Identify critical failure modes before building all components.

Stage 03

Agent Build & Integration

Weeks 7–16

Build each specialist agent. Wire tool integrations. Implement state management. Build human-in-the-loop interfaces. Run evaluation suites continuously.

Stage 04

Production & Operations

Final 3 weeks

Deploy to your cloud. LangSmith observability. SLO-based alerting. Runbooks. Monitoring period before full autonomous operation.

Every question answered.

Can't find what you need?

Agentic AI refers to AI systems that operate autonomously over extended task sequences — making decisions, taking actions, adapting to outcomes, and pursuing goals without constant human direction. In practice this means multiple AI agents working in orchestrated pipelines, each with specialist capabilities.

Complex workflows requiring parallel processing, specialist domain knowledge across multiple areas, or tasks too long for a single context window. Each agent is optimised for its task; the orchestrator ensures coherence across the whole system.

Two-to-three agent pipeline: 10–14 weeks to production. Full enterprise multi-agent system: 16–24 weeks. Evaluation framework and integration complexity are usually the biggest variables.

When a single LLM call solves the problem. When the workflow is deterministic. When latency requirements are under 500ms. When the task does not require judgment across multiple domains. We will tell you if a simpler approach is right.

Yes. We have production integrations with Salesforce, SAP, Xero, Zendesk, SharePoint, Confluence, Jira, and most enterprise systems with REST APIs. Sub-agents have scoped access — keeping integrations auditable.

Every agent action is logged via LangSmith. High-risk operations require human-in-the-loop confirmation. Agent permissions are scoped at the tool level. We build the evaluation framework before the system — so you can measure safety improvements.

An AI agent is a single autonomous system with tools and a goal. Agentic AI describes systems where multiple agents collaborate: an orchestrator breaks down a complex objective, delegates to specialist agents, aggregates outputs, handles failures, and iterates. The agent is a component; agentic AI is the architecture.

A two-to-three agent pipeline: AUD $80,000–$160,000. A full enterprise multi-agent system: $160,000–$400,000+. Fixed-scope quotes after a free technical discovery session — no surprises mid-project.

LangGraph is our primary framework. Its graph-based execution with explicit state, cycle support, and conditional branching is what makes reliable orchestration possible at scale. We have been building LangGraph systems since early availability.

LangSmith tracing for every agent decision and handoff, dashboards for token usage and latency, SLO-based alerting, and evaluation suites that run on every deployment. Agentic systems require more rigorous observability than standard software.

Always yours. Every system runs inside your AWS VPC via Bedrock. Your data, agent state, and orchestration logs never leave your environment.

Legal and compliance, financial services, retail and supply chain, software engineering, and professional services — wherever multi-step reasoning across specialist domains is required.

1 Your Information

2 Book Meeting

3 Confirmation

Architect your agentic AI system.

45 minutes. We'll assess your workflow complexity, recommend the right architecture, and give you an honest view of what's achievable, what it costs, and what the risks are.

What to expect on your call

What to expect

1 Share a few details
Complete the form with your contact details and what you need help with.
2 Book your free discovery call
Once you submit the form, choose a time that suits you for your discovery call.
3 Privacy comes first
Sign an optional NDA to ensure the highest privacy level and protection of your idea.
4 Discovery call
We’ll discuss your goals, the support you need and answer your questions. If we’re a good fit, we’ll outline the next steps.

What to expect

1 Share a few details
Complete the form with your contact details and what you need help with.
2 Book your free discovery call
Once you submit the form, choose a time that suits you for your discovery call.
3 Privacy comes first
Sign an optional NDA to ensure the highest privacy level and protection of your idea.
4 Discovery call
We’ll discuss your goals, the support you need and answer your questions. If we’re a good fit, we’ll outline the next steps.

When one agent isn't enough, orchestrate many.