Agentic AI Systems & Orchestration
Build AI agents that use tools, workflows, approvals, and enterprise systems safely.
BUILD
Agents and systems that hold in production.
We build AI agents, RAG systems, automation workflows and AI-native software platforms that connect to enterprise data, tools, workflows and systems of record, with evaluation, governance and observability built in from day one, and security controls integrated across identity, permissions, data access and deployment.
The short answers.
- How does this fit our architecture?
On top of your systems of record: knowledge, orchestration and application layers, with evaluation, governance, security and observability across every layer.
The four layers →- What gets built?
Almost always one of three shapes: answers drawn from your own records, an agent that acts within an authority you set, or an application people use all day.
The three patterns →- What changes in the technology estate?
New components, each with a named owner: a working system and its source code, an integration and API layer, an evaluation harness, and runbooks to operate them.
What you receive →- How is AI integrated and governed?
Through approved tools with identity-scoped permissions. Irreversible or costly actions wait for a person, and every action leaves an audit trail.
The governed agent →- How do we avoid another isolated platform?
It runs where your data, identity and controls already live (your cloud, a private VPC or hybrid), with the model provider kept swappable.
Where it runs →
The business problem
Agentic AI & Engineering
Demos are easy. Systems that hold inside enterprise environments, with security, integration, evaluation, and observability, are the hard part.
Why current approaches fail
Calling a model is easy. Everything around it is the work.
Any engineer can get an answer out of a foundation model this afternoon. What takes months is everything that has to be true before that answer can be acted on inside a business: that it drew on governed data the caller is allowed to see, that it can be traced back to a source, that it did not quietly get worse last week, and that somebody owns it when it does.
That is not one problem. It is a systems problem across enterprise data, permissions, context, models, agents, workflows, applications, evaluation, security, governance and human oversight, and it fails at the joins, not at the parts. Captivolt engineers across those boundaries.
Captivolt point of view
We build AI applications as enterprise systems, not isolated model integrations.
- Context and permissions are designed in.
Where context comes from, and who may read it, are architecture decisions rather than filters added after retrieval.
- Evaluation and observability are wired in from the first build.
A system measured from its first release can be improved. One measured after an incident can only be explained.
- The client owns what is built.
Pipelines, configuration and evaluation datasets stay with the team that has to run them.
Reference patterns
Three shapes, and almost everything is one of them.
What we build, drawn end to end. Each names where context comes from, where the model sits, who is allowed to act, and what is left behind afterwards, because those are the questions an isolated model integration cannot answer.
Pattern A: Enterprise Knowledge Intelligence
Answers drawn from your own records, with the passage each claim came from attached.
The right shape when the work is knowing rather than doing: policy, precedent, contracts, support history. It answers; it does not act, which is what keeps it cheap to govern.
Pattern A · knowledge
Enterprise datasystems of record · documents · warehouse
Permissioned retrievalthe caller’s access, not the service’s · hybrid search · rerank
Context assemblychunking · metadata · lineage · freshness
Grounded reasoningno answer the retrieved evidence does not support
Cited answerevery claim traced to its passage · retained with the response
Continuous evaluationgroundedness · retrieval precision · regression suite
RETRIEVAL QUALITY MONITORING · POLICY CHECKS · PROVENANCE
Pattern B: Governed Agent
Acts inside your systems, within an authority you set, leaving evidence of every action.
The right shape when something must happen, not just be answered. The approval step is sized by consequence: irreversible or costly actions wait for a human, routine ones do not.
Pattern B · agent
Inbound signalevent · request · schedule
Context assemblythe policy, records and history the decision needs
Agent reasoningplan · tool selection · checked against the task it was given
Approved toolsregistry · identity-scoped permissions · rate and cost limits
Human approvalwhere the action is irreversible or expensive to undo
Action executedwritten to the system of record, not to a transcript
Evidence retainedper-action audit trail · inputs, decision, outcome
EVALUATION SUITE · AUTHORITY BOUNDARIES · MONITORING & ALERTING
Pattern C: AI Application
Software people use all day, with the model as one component inside it rather than the product.
The right shape when AI is a feature of a workflow rather than the whole of it. Most of the engineering here is the ordinary kind (state, latency, cost, failure modes), which is exactly why model-first builds stall at this point.
Pattern C · application
Experience layerweb · mobile · embedded in the workflow · the user’s request
API & orchestrationintegration with enterprise systems · session and state
Context layerretrieval over permitted data · caching
Model layerrouting · fallback · the boundary kept swappable
Control planeinput and output policy · guardrails · release gate on evaluation
Response servedanswer returned · request trace retained
Monitoring & feedbacklatency · cost · drift · what users actually did next
TENANCY · COST ATTRIBUTION · INCIDENT PATH
Video · 3 min
From question to governed action.
The Governed Agent pattern, followed through one illustrative question: who is asking and what they may see, the context assembled and filtered at the permission boundary, reasoning that cites its evidence, claims checked before the answer, and an action that waits for a person to approve it.
Enterprise agent – from question to governed action · 3:03
Read the video as text
- The business question. In the Captivolt account workspace, an account manager working on the Meridian Health account types: “What is the highest-priority expansion opportunity for this customer over the next 90 days, and what should I do next?” “A business question is not just a prompt.”
- Identity and intent. The user, an account manager, is verified through SSO; their role scopes them to their own accounts; intent detection reads the request as expansion planning, and the Account Intelligence Agent is selected by that intent. “Who is asking? What are they allowed to know? Which capability should respond?”
- Building the context. CRM facts, contracts and product usage become facts; meeting notes, support signals and delivery signals become semantic evidence; relationship history and open opportunities become relationships. The agent receives them assembled. “Facts + Evidence + Relationships + Business Signals.”
- Filtering the context. A board-restricted M&A memo and an HR compensation file are blocked at the permission boundary. What passes is ranked: the September product usage report (fresh, high relevance), the Q3 executive meeting notes (relevant, recent), the master services agreement (current terms), and a 2021 pricing proposal (stale, deprioritised). “Access-aware. Relevance-ranked. Freshness-aware.”
- Reasoning under policy. An evidence evaluation trace: opportunity signal, contract timing, usage expansion and relationship strength each support the recommendation and cite their evidence; delivery risk is flagged and monitored. No recommendation without supporting evidence. “Recommendations must be supported by evidence.”
- Claim validation: generate, validate, support. Of the draft response’s three claims, “Product X usage is expanding across the account” is supported by E1 and E2, and “Renewal timing creates a 90-day expansion window” by E3; “Budget for Product X is already approved” has insufficient evidence and is struck out. The evidence set: E1, the September product usage report; E2, the CRM opportunity record; E3, the MSA renewal clause; E4, the Q3 executive meeting notes; E5, support and delivery tickets.
- The executive answer. A validated recommendation from the Account Intelligence Agent: expansion into Product X. Why now: usage expansion, contract timing and relationship strength. Confidence: high. Evidence: five sources. Recommended next action: schedule an executive discovery meeting. “Not just an answer. A supported recommendation.”
- Action control. The agent proposes creating a CRM opportunity (type expansion, product Product X) on the basis of the validated recommendation. The approval gate requires a person: a sales manager approves, and the approval is logged. The workflow then completes: the opportunity is created, an owner assigned and an audit event recorded. “Intelligence can be automated. Authority remains controlled.”
- “Enterprise agents should not just generate answers. They should operate with context, evidence and control.” Captivolt: production AI engineering and assurance.
Technical depth
How we build production AI.
Enterprise AI requires more than a model. Captivolt designs the full operating architecture: data, knowledge, tools, agents, applications, evaluation, governance, security, and observability.
ERP, CRM, ITSM, HRMS, document stores, databases and APIs: the systems of record AI must respect.
Ingestion, indexing, semantic modelling, metadata, permissions and lineage, engineered so AI answers from governed truth.
LLMs, tools, agent orchestration, workflow rules and human approvals: controlled autonomy, by design.
Copilots, dashboards, automations, portals and internal applications, where AI meets real work.
Evals, prompt regression, retrieval quality, hallucination checks and task success: VeriCore patterns wired into delivery.
AI inventory, risk classification, approval workflows, audit evidence and oversight cadence: AegisIQ in operation.
Access control, data classification, agent authority boundaries, prompt-injection resistance, incident response.
Monitoring, drift detection, cost tracking, latency, feedback, and continuous improvement.
Dashed layers wrap the whole stack: evaluation, governance, and operations are designed in, not added later.
Engineering decisions we design with you
- Model-provider optionality
- Vector database optionality
- Cloud deployment options
- Identity & access integration
- API gateway / tool registry
- Prompt & version management
- Evaluation dataset management
- Observability stack
- Data retention approach
- Cost monitoring
- Incident response
Capabilities
What this pillar covers.
AI Agents & Automation
AI Agents & Multi-Agent Workflows
Design role-specific agents that collaborate across tasks, systems, and decision points.
Enterprise RAG & Knowledge Systems
Create permission-aware retrieval systems with ingestion, indexing, grounding, access control, and source traceability.
GenAI Applications & Copilots
Design and build copilots, assistants, and GenAI applications embedded in real operational workflows.
AI-native Software Engineering
Build custom applications, APIs, workflow interfaces, and backend services that operationalise AI.
Technology Architecture & Modernisation
Modernise legacy systems, integration layers, and architecture foundations for AI readiness.
AI-enabled SDLC Transformation
Embed AI into requirements, design, coding, testing, release validation, and support.
Enterprise Integrations
Connect AI systems to ERP, CRM, ITSM, HRMS, data platforms, document stores, and internal applications.
Enterprise Context & Intelligence
The context layer AI reasons over, grounded, permissioned and current, on AI-ready data foundations, semantic models and analytics.
LLMOps / AgentOps
Build lifecycle infrastructure for models, prompts, agents, evaluations, deployments, and monitoring.
Infrastructure as Code & Platform Automation
Implement repeatable infrastructure provisioning and environment management for AI platforms.
AI-ready Cloud & DevOps Engineering
Design CI/CD, release governance, environment strategy, and DevOps foundations for AI-enabled systems.
AI Observability & Cost Engineering
Monitor model behaviour, latency, cost, quality, token usage, and operational reliability.
Use cases
Typical engagements.
- Enterprise knowledge assistant
- Agentic workflow automation
- AI copilot for operations teams
- Document intelligence platform
- Legacy modernisation for AI readiness
- AI-enabled software delivery lifecycle
- AI platform / LLMOps foundation
How Captivolt delivers
The engagement, and what you are left with.
ENGAGEMENT SHAPE
Typically a scoped PoC sprint (4–6 weeks), followed by production build pods and structured capability transfer.
What makes this different
- Production architecture from day one
- Software engineering plus AI engineering
- Built-in QE, governance, and security hooks
- Handoff-ready documentation and runbooks
- Designed for enterprise systems of record, not sandbox demos
- An operating model, not just a system: who runs it, who approves a change, who is on call
What you receive
- Working production system and source code
- Reference architecture and design records
- Integration and API layer
- Evaluation harness wired to VeriCore patterns
- Runbooks and handover documentation
- Named ownership for every component, and the operating model it runs under
Deployment
Built for your environment, not ours.
Production AI has to run where your data, identity, and controls already live.
- Client cloud
- Private VPC
- Hybrid deployment
- Model-provider optionality
- Enterprise identity integration
- Audit and monitoring layer
Evidence
Where we have done this.
- BUILD
- ASSURE
Enterprise AI QE Architecture
Proprietary framework- Context
- GenAI systems routinely pass demos and fail in production, because they are not tested like enterprise software.
- Challenge
- LLM, RAG, and agentic systems need evaluation disciplines that traditional QE does not provide.
- What Captivolt delivered
- Evaluation architecture · dataset design patterns · regression suite structure · scorecard model · monitoring approach.
- What it provides
- AI quality became evidence rather than opinion: one repeatable architecture for testing LLM, RAG and agentic systems before and after release.
- AI-QE lifecycle
- Evaluation scorecard (concept)
- Regression suite design
- BUILD
Agentic RAG Framework
Reference architecture- Context
- Enterprises need knowledge systems that answer accurately, respect permissions, and can be observed and improved in production.
- Challenge
- Naive RAG implementations leak data, hallucinate, and degrade silently.
- What Captivolt delivered
- Reference architecture · permission-aware retrieval model · grounding and traceability design · evaluation and observability loop.
- What it provides
- A production-grade RAG pattern teams can adopt, extend and operate without us.
- RAG reference architecture
- Permission model
- Evaluation loop design
Relevant accelerators
What carries this work.
- Agentic RAG Accelerator →ENTERPRISE RETRIEVAL & GROUNDING
It is how the BUILD pillar delivers retrieval and grounding, rather than a separate purchase.
- AI-Native SDLC Blueprint →AI-ASSISTED SOFTWARE DELIVERY
It is the delivery discipline behind the BUILD pillar, applied to your own engineering organisation.
Further reading
Request an Architecture Walkthrough.
A structured first conversation about what you are trying to build, govern, or scale.