Agentic AI
AI That Doesn't Just Answer. It Acts.
A chatbot that answers a question is useful. An agent that reads the question, checks three systems, makes a decision within defined boundaries, takes an action, and reports back what it did - that's a different category of system, and a different category of business value.
Agentic AI systems plan, use tools, make decisions, and complete multi-step tasks with limited human intervention. We design and build agentic AI systems that handle real operational workflows - with the guardrails, evaluation, and human oversight that keep them safe and reliable in production.
Agentic AI Services Built for Real Operations
Agentic AI spans a range of capabilities and architectures. Here's what we design and build, matched to the complexity your use case actually requires.
AI Agent Architecture & Design
Before any code is written, we design the agent's reasoning approach, tool access, decision boundaries, memory strategy, and escalation paths. This is the single most important determinant of whether an agentic system is safe and reliable - and the step most commonly skipped.
→ Safe, reliable agents start with deliberate architecture
Focus Areas
Single-Agent Task Automation
Agents that handle a well-defined, repeatable task end-to-end - processing a request, validating data against business rules, taking an action in a system, and reporting the outcome. The right starting point for most businesses new to agentic AI.
→ Prove agentic AI on one bounded task first
Focus Areas
Multi-Agent Orchestration
Systems where multiple specialised agents collaborate - a research agent, a validation agent, an execution agent - coordinated by an orchestrator that manages task handoffs, shared context, and failure recovery. Built using LangGraph, CrewAI, and AutoGen.
→ Specialised agents working as a coordinated team
Focus Areas
Tool Use & Function Calling Integration
Agents that can query your databases, call your internal APIs, search the web, read and write documents, and operate your existing software tools - with clearly scoped permissions so an agent can only take actions it's explicitly authorised to take.
→ Agents connected to your real systems, with scoped permissions
Focus Areas
Agentic RAG (Research & Retrieval Agents)
Agents that don't just retrieve a single answer but iteratively search, cross-reference multiple sources, evaluate confidence in what they've found, and decide whether they have enough information to act or need to ask a clarifying question.
→ Research and retrieval before action, not guesswork
Focus Areas
AI Agent Guardrails & Safety Engineering
Output validation, action approval workflows, rate limiting, scope restriction, hallucination detection, and human-in-the-loop checkpoints for high-stakes actions. Non-negotiable infrastructure for any agent that takes real-world actions.
→ Safety infrastructure built in from day one
Focus Areas
What Businesses Are Actually Using Agentic AI For
Agentic AI is most valuable where a task involves multiple steps, requires checking or combining information from more than one source, and follows rules that can be clearly defined.
Customer Support Resolution
Agents that don't just answer FAQ-style questions but actually resolve issues - checking order status across systems, processing eligible refunds, updating account details, and only escalating to a human when the situation falls outside defined policy boundaries.
Sales & CRM Automation
Agents that research a prospect across multiple data sources, enrich CRM records automatically, draft personalised outreach, schedule follow-ups, and flag deals that show risk signals - handling the research and data work that consumes sales team time.
Procurement & Vendor Management
Agents that monitor inventory levels, check vendor pricing and availability across multiple suppliers, generate purchase orders within approval thresholds, and escalate exceptions - reducing the manual coordination overhead in procurement workflows.
Financial Operations & Reconciliation
Agents that cross-reference invoices against purchase orders and receipts, flag discrepancies, process routine reconciliations, and prepare exception reports for human review - handling the high-volume, rule-based work in finance operations.
IT Operations & Incident Response
Agents that triage incoming alerts, correlate them against known issues and recent changes, attempt defined remediation steps, and escalate to on-call engineers with full context when automated resolution isn't appropriate.
HR & Employee Onboarding
Agents that handle the administrative sequence of onboarding - provisioning accounts, sending required documentation, scheduling orientation sessions, and tracking completion - freeing HR teams to focus on the parts of onboarding that need a human touch.
What Makes Working With Us Different
Agentic AI is one of the most hyped areas of technology right now, and also one of the easiest to get wrong in ways that aren't obvious until an agent has already taken a wrong action.
We scope agent autonomy deliberately
Not every action should be autonomous. We work with you to define exactly which decisions an agent can make independently, which require human approval, and which are off-limits entirely. This boundary-setting happens before development, not as a patch after something goes wrong.
We design for failure, not just success
What happens when the agent isn't confident in its answer? When a tool call fails? When it encounters a situation outside its training? We build explicit handling for these cases - graceful degradation, clarifying questions, and human escalation - rather than assuming the happy path is what will happen.
Every agent action is logged and auditable
When an agent takes an action on your behalf, you need to know what it did, why it decided to do it, and be able to trace that decision after the fact. We build comprehensive audit trails into every agentic system - essential for trust, debugging, and compliance.
We evaluate rigorously before deployment
We test agents against a structured set of scenarios - including edge cases, ambiguous instructions, and adversarial inputs - before they touch production systems or real customers. A demo that works on the happy path tells you almost nothing about production readiness.
We start narrow and expand scope deliberately
The right way to deploy agentic AI is to start with a well-bounded task, prove it works reliably with real usage, and then expand scope incrementally. We don't recommend launching a broad, highly autonomous agent on day one - and we'll push back if that's what's being asked for.
We're honest about what agentic AI can't do yet
Current agentic systems are genuinely capable, but they're not magic. They struggle with tasks requiring deep judgment in ambiguous situations, and they can fail in ways that are hard to predict. We'll tell you clearly where the technology's limitations mean a different approach is more appropriate.
How We Approach an Agentic AI Engagement
Agentic AI projects require more upfront boundary-setting and evaluation work than typical software or even standard LLM projects - because the system is taking actions, not just producing outputs.
Typical pilot timeline
for a single, well-bounded agent with guardrails and human oversight
Task & Boundary Definition
We work with you to define exactly what task the agent will perform, what tools and systems it needs access to, what decisions it can make autonomously, what requires human approval, and what's explicitly out of scope. This boundary-setting is the foundation everything else is built on.
- Task specification
- Autonomy boundary document
- Tool access requirements
- Escalation criteria
Architecture & Tool Integration Design
We design the agent architecture - single agent or multi-agent, the reasoning approach, memory and context management strategy, and the specific integrations needed to connect the agent to your systems (APIs, databases, internal tools).
- Architecture diagram
- Tool integration plan
- Technology selection
Development & Guardrail Implementation
We build the agent alongside its safety infrastructure simultaneously - output validation, action approval workflows where required, rate limiting, and monitoring hooks. Guardrails are not an afterthought added before launch; they're built in from the first working version.
- Working agent system
- Guardrail implementation
- System integrations
Evaluation & Red-Teaming
Structured testing against a comprehensive scenario set - typical cases, edge cases, ambiguous instructions, and deliberately adversarial inputs designed to find where the agent behaves unpredictably. We don't consider an agent ready based on a handful of successful demo runs.
- Evaluation report
- Failure modes & mitigations
- Go/no-go recommendation
Pilot Deployment with Human Oversight
We deploy to a limited, real-world pilot with active human oversight - reviewing agent decisions, catching issues early, and building confidence before expanding scope. This is where theoretical evaluation meets actual operational reality.
- Pilot deployment
- Oversight dashboard
- Pilot performance report
Scaled Deployment & Ongoing Monitoring
Based on pilot results, we expand scope and autonomy incrementally, with continuous monitoring for behavioural drift, cost tracking, and action audit review. We set up the infrastructure for ongoing evaluation, not just a one-time launch checklist.
- Production deployment
- Monitoring dashboards
- Audit trail system
The Frameworks and Platforms We Work With
Agentic AI tooling is evolving quickly. We work across the leading frameworks and choose based on your specific orchestration, reliability, and integration requirements.
Foundation Models
OpenAI (GPT-4o, o1, o3), Anthropic Claude (Sonnet, Opus), Google Gemini, Meta Llama, Mistral - selected for reasoning quality, tool-use reliability, and cost per task.
Agent Orchestration Frameworks
LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Semantic Kernel - for single and multi-agent system design.
Tool & Function Calling
Native function calling APIs, Model Context Protocol (MCP), custom tool integration layers for internal systems and APIs.
Memory & Context Management
Vector databases (Pinecone, Weaviate, Qdrant) for long-term memory, Redis for session state, structured context windows for multi-turn agent reasoning.
Evaluation & Testing
LangSmith, Braintrust, custom evaluation harnesses, adversarial test suites, human-in-the-loop review workflows.
Observability & Monitoring
LangSmith, Arize AI, custom logging pipelines for action audit trails, token usage tracking, and behavioural drift detection.
The People You Need, Ready When You Need Them
Building agentic AI systems well requires people who understand both the capabilities and the failure modes of autonomous AI - not just standard software engineers applying an LLM API.
Agentic AI Architects
Senior specialists who design the agent's reasoning approach, autonomy boundaries, tool access scope, and overall system architecture before development begins - the most consequential decisions in any agentic project.
LLM & Agent Engineers
Engineers who build the agent logic, integrate orchestration frameworks, implement tool-calling, and handle the prompt and context engineering that determines how reliably an agent performs its task.
AI Safety & Guardrail Engineers
Specialists focused specifically on output validation, action approval workflows, rate limiting, and failure mode mitigation - the infrastructure that keeps autonomous systems safe in production.
Integration Engineers
Engineers who build the connections between agents and your real systems - APIs, databases, internal tools - with carefully scoped permissions so agents can only take explicitly authorised actions.
Evaluation & QA Engineers
Specialists who design and run structured evaluation frameworks, build adversarial test scenarios, and continuously monitor deployed agents for behavioural drift and emerging failure patterns.
MLOps Engineers (Agentic Systems)
Engineers who build the production infrastructure specific to agentic AI - audit logging, cost monitoring, agent versioning, rollback capability, and the observability layer that standard application monitoring doesn't cover.
What Well-Built Agentic AI Actually Delivers
Agentic AI is justified by what it lets your business do that it couldn't do before - not by the sophistication of the technology.
End-to-End Task Completion, Not Just Information
Unlike a standard chatbot or search tool, agents complete the actual task - updating a record, processing a request, taking an action - removing the step where a human has to read an AI's output and then go do the work manually.
Significant Reduction in Multi-Step Process Time
Workflows that require checking multiple systems, cross-referencing data, and making a decision based on combined context - work that previously took a person 20–30 minutes per case - can often be completed by an agent in seconds, with human review only for genuine exceptions.
Consistent Application of Business Rules
Agents apply defined policies and rules the same way every time, without the variability that comes from different staff members interpreting guidelines differently - particularly valuable for compliance-sensitive and approval-based processes.
Faster Response Times at Scale
Agentic customer service and operational systems can handle volume spikes without the lag that comes from hiring and training additional staff - important for businesses with seasonal demand or rapid growth.
Freed Capacity for Higher-Value Human Work
When agents handle the routine, well-defined portion of a workflow, your team's time shifts toward the judgment calls, relationship management, and complex problem-solving that actually require a person.
A Foundation That Scales With Confidence
Because well-built agentic systems include audit trails, evaluation frameworks, and clear autonomy boundaries, you can expand their scope over time with evidence of reliability - rather than hoping a black-box system continues working as you give it more responsibility.
Work With Us the Way That Fits Your Situation
Agentic AI engagements range from a focused feasibility assessment to ongoing management of a fleet of production agents. We structure the work to match where you are.
Agentic AI Feasibility Assessment
2–4 weeks
A focused engagement where we evaluate whether your candidate use case is genuinely suited to an agentic approach, assess what tool access and data the agent would need, define realistic autonomy boundaries, and give you a clear recommendation and cost estimate before you commit to building.
What's included
- Use case suitability assessment
- Tool access and integration scoping
- Autonomy boundary recommendations
- Risk assessment for the proposed agent actions
- Costed build recommendation
Pilot Agent Development
6–10 weeks
A focused build of a single, well-bounded agent deployed to a limited pilot with active human oversight. The right starting point for most organisations - proves the concept works reliably before expanding scope or building multi-agent systems.
What's included
- Agent design and guardrail implementation
- Tool and system integration
- Evaluation and red-teaming before launch
- Pilot deployment with oversight dashboard
- Performance report and scale-up recommendation
Full Agentic System Development
12–20+ weeks
End-to-end development of a multi-agent system or a fleet of task-specific agents, including full orchestration, comprehensive guardrails, evaluation infrastructure, and production MLOps. For organisations that have validated the approach and are ready to deploy at scale.
What's included
- Multi-agent architecture and orchestration
- Full guardrail and safety infrastructure
- Comprehensive evaluation framework
- Production deployment with monitoring
- Audit trail and compliance reporting
Embedded Agentic AI Team Augmentation
Ongoing
Senior agentic AI engineers join your existing team directly - working in your tools, your sprints, and your codebase. Best for product companies building agentic features into their own product who need specialised expertise integrated into their existing engineering org.
What's included
- Pre-vetted senior agentic AI specialists
- 48-hour onboarding into your stack
- Full integration with your engineering workflow
- Full-time or part-time availability
- Flexible scaling with 30 days notice
How much does agentic AI development cost?
Pricing depends on the number of agents, integration complexity, and autonomy level. Here's a realistic breakdown - we'll give you a detailed estimate after understanding your specific use case.
| Scope | Typical Timeline | Indicative Investment |
|---|---|---|
| Agentic AI Feasibility Assessment | 2–4 weeks | $8,000 – $20,000 |
| Pilot Agent (single, well-bounded use case) | 6–10 weeks | $30,000 – $80,000 |
| Full Multi-Agent System | 12–20+ weeks | $100,000 – $350,000+ |
| Embedded Team Augmentation | Ongoing | Custom monthly rate |
Frequently Asked Questions
Straight answers about safety, cost, scope, and what to expect before you commit to building.
Let's Talk About What You Want an AI Agent to Actually Do
Tell us the process or task you're considering automating with an agent - the steps involved, the systems it would need to touch, and what decisions it would need to make - and we'll have a direct, technically grounded conversation about whether agentic AI is the right approach and what it would take to build well.
We respond within one business day and can have a technical discovery call scheduled within the week.