Core Practice

Agentic Engineering: Autonomous AI Systems That Drive Your Operations

We design, build, and operate autonomous AI agent systems that automate your most complex workflows (customer ops, finance, procurement, onboarding) and run reliably in production without a queue.

From pilot to live in 2–4 weeks, handling 4× your current volume with the same team. Connected to your existing stack, auditable by design.

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2–4 weeks

to a production-ready pilot

60–80%

reduction in manual effort

24/7

autonomous operation, no queues

How We Build

The agentic engineering approach

We design each agent system around a clear agentic framework: the planning loop, tool access, memory strategy, and context engineering that determines what the agent knows and when. We integrate via MCP tools and standard APIs, build evaluation harnesses to measure real-world performance, and apply RL-based agent training where agents improve from production feedback.

Every system ships with human-in-the-loop escalation paths, full audit trails, and a production observability layer, not just a working demo.

Discuss your workflow

Agentic framework

ReAct, tool-use, memory, planning loops

Each agent is built around an explicit reasoning architecture, not a prompt chain, a designed system.

Context engineering

What the agent knows and when

We design the information architecture agents reason over: retrieval strategy, chunking, memory scoping.

MCP + evaluation harness

Integrated tools, measurable output

Agents connect to your systems via MCP tools. Every deployment includes an evaluation harness to track real-world performance.

Four types of agent

These cover the majority of ops automation needs and can be combined into multi-agent pipelines for more complex workflows.

Multi-step task agents

Handle a complete workflow from trigger to outcome: data extraction, decision, action, confirmation, across multiple systems in a single automated run.

Decision & routing agents

Classify, score, prioritise, and route work items at high volume. Consistent decisions applied thousands of times a day, no human queues.

Integration agents

Connect your CRM, ERP, helpdesk, and comms tools via MCP tools and API connectors so work flows between systems automatically, no custom middleware required.

Human-in-the-loop systems

Agents that know their limits. Edge cases, low-confidence decisions, and exceptions are escalated to humans with full context already attached.

What You Get

Business outcomes, not infrastructure buzzwords

High-volume routine work handled without growing headcount

Consistent, auditable decisions applied at speed and scale

Workflows that run 24/7, not blocked by team capacity

Processing time cut from days to minutes

Full audit trail on every automated action for compliance

Automation rate improves over time via RL-based agent training

Workflows we automate

Across ops, finance, sales, and compliance, each typically scoped as a 1–4 week pilot.

Customer Operations

Route, classify, and resolve tier-1 support tickets. Draft responses, trigger refunds, escalate edge cases, all within your existing helpdesk.

Finance & Accounts Payable

Extract invoice data, match POs, flag exceptions, and route approvals, reducing manual data entry and processing backlogs.

Procurement

Automate RFQ creation, vendor follow-ups, and approval workflows. Agents keep procurement moving without chasing stakeholders manually.

Sales Operations

Qualify inbound leads, enrich CRM records, draft follow-up emails, and surface next-best actions for sales reps.

Employee Onboarding

Guide new hires through tasks, answer policy questions, provision access requests, and send reminders automatically.

Disputes & Chargebacks

Gather evidence, classify dispute type, draft responses to payment networks, and track resolution status without manual queues.

Contract Review

Extract key terms, flag non-standard clauses, summarise obligations, and route for legal sign-off in minutes, not days.

Compliance Monitoring

Continuously scan transactions, communications, or documents against policy rules and surface exceptions for human review.

Agentic Framework & Ecosystem

Cloud & Infra

AWSGCPAzure

Models

OpenAIClaudeGeminiSarvam

Agentic Frameworks

LangGraphLangChainLlamaIndexCrewAIAutoGenSemantic Kernel

Memory, Eval & Observability

WeaviatePineconeRedisLangfuseRagasMCP

Which workflow should you automate first?

Start with one painful process. We'll scope the agentic system, build it, and have agents running in production within weeks.

Start a Pilot

Frequently asked questions

These are three distinct form-factors, not interchangeable marketing terms. Automation (including RPA) handles repeatable, rule-based tasks: it follows a fixed script. Assistants help knowledge workers retrieve, summarise, and navigate information, like a conversational interface over your documentation. Agents go further: they make decisions, select tools, and take actions across multiple systems to complete multi-step goals autonomously. Choosing the wrong form-factor for a use case is one of the leading reasons AI projects are cancelled before they reach production. We scope each engagement by matching the workflow to the right approach, not defaulting to the most complex option.

One to four weeks for a scoped production pilot. We start with one high-volume, painful process, not a proof-of-concept in a sandbox. By week four you have agents running on real workloads, a measured baseline (task adherence, exception rate, processing time), and a clear case for expanding.

Yes. Agents connect to your existing stack via API and MCP tool integrations: Salesforce, SAP, Zendesk, ServiceNow, NetSuite, and others. We design integration layers before granting any write permissions, following least-privilege access principles so agents only touch what they need to.

Every system we build includes an observability layer that answers three questions in real time: what the agent did, why it did it, and whether it is getting better or worse over time. We instrument task adherence, tool-call accuracy, and exception rates from day one, so you have a dashboard, not a black box.

Human-in-the-loop escalation is a first-class design requirement, not an afterthought. We configure approval tiers: agents handle routine cases autonomously, escalate low-confidence decisions to a human with full context pre-attached, and never take irreversible actions without appropriate sign-off. Autonomy is expanded gradually as trust is established.

Pilots are scoped and fixed-fee, typically in the $7k–$14k range depending on integration complexity. Ongoing production operation is a monthly managed service. We don't charge by the hour; you know the cost before we start.