Enterprise Knowledge Agents
Agents that retrieve, reason, and respond using governed internal knowledge sources.
SOLUTION
Design, deploy, and govern AI agents that work inside real enterprise workflows.
Captivolt builds AI agents that reason over enterprise knowledge, call approved tools, follow workflow rules, escalate to humans, and generate evidence for monitoring and governance.
The short answers.
- How do we industrialise AI?
Every agent runs the same ten steps on every request (scoped intent, permissioned context, a policy check, registered tools), and each step writes down what it did.
The agent anatomy →- How do we standardise RAG and agents?
Through one engineered context layer: governed records and approved sources, retrieved under the caller’s identity and permission scope, with provenance kept.
Context engineering →- How do we manage evaluation?
With VeriCore, before release and after it: agents are scored on task completion, tool selection and instruction adherence.
The accelerators →- How do we establish reusable AI capabilities?
The context layer, evaluation harness and controls are built once, so the second agent inherits them instead of starting again.
Non-negotiables →
The business problem
Enterprises want AI that does work inside workflows, not only answers questions.
Once an AI system can call tools, update records and trigger actions, a wrong answer becomes a wrong action, taken with whatever access the agent was given.
Why current approaches fail
Agents that work in a demonstration and fail in production.
- More access than the task needs
An agent running under a shared service account can reach far more than the person it is acting for.
- Built once, rebuilt for every agent
Context, evaluation and controls are built for the first agent and not shared, so the second one starts again.
- No record of what it did
When an action is questioned, nobody can reconstruct which data it read, which tool it called or who approved it.
Captivolt point of view
An agent that can act changes the risk. Authority boundaries, approved tools, human approval points and traceability are what make autonomy something an enterprise can accept.
- Authority is held outside the model.
What an agent may do is checked against policy on every run, whatever it is asked to do.
- Every step leaves a record.
A run that cannot be reconstructed afterwards cannot be governed.
Agent anatomy
What happens every time an agent runs.
Ten steps, each one writing down what it did. This is the execution lifecycle: not how an agent gets built, which is further down the page, but what it does in production, on every request, forever.
- 01
Trigger / intent
An event, a request or a schedule starts the run, and the intent is resolved to something this agent is scoped for. An agent with no scope check accepts every job it is handed.
RecordsThe trigger, the requester, and the intent as resolved
- 02
Context
The records, policy and history this decision needs, retrieved under the requester’s permissions, not the agent’s service account.
RecordsWhat was retrieved, from where, at which version
- 03
Reasoning
A plan: what to do, in what order, with what. Reasoning is one step rather than the whole system, and the steps around it are what make it safe to run.
RecordsThe plan, and what it chose against
- 04
Policy
The plan is checked against what this agent may do at all: data classes, systems, value limits, time of day. A plan that fails here is revised or stopped. It is not negotiated with.
RecordsThe rule applied and what it returned
- 05
Tool selection
Tools come from a registry rather than from the model’s imagination, and inputs are validated against a schema before any call is made.
RecordsThe tool, its inputs, and the authority that permitted it
- 06
Human approval, where required
Sized by consequence. Irreversible or expensive actions wait for a person; routine ones do not, because an approval nobody reads is worse than no approval at all.
RecordsWho approved, when, and what they were shown
- 07
Action
Executed against the system of record. The agent writes where a person would have written, under an identity that can be traced back to this run.
RecordsThe call, the result, and the identity it acted under
- 08
Verification
Did it do what it said? The write is read back and checked against the intent, because a 200 from an API is evidence that a call succeeded, not that the outcome was right.
RecordsThe check performed and what it returned
- 09
Audit evidence
The run becomes a record (intent, context, plan, policy result, approval, action, verification), assembled as it goes. A trace reconstructed afterwards is a story about a run rather than the run.
RecordsThe trace itself, retained against the system’s register entry
- 10
Learning
Failures, refusals and escalations become evaluation cases. Nothing about the agent is adjusted in production on the strength of one bad run.
RecordsWhat entered the evaluation suite, and why
What the agent carries
State and memory are routinely treated as the same thing. One is discarded with the run; the other has a retention policy and an owner, and that difference is where agents quietly go wrong.
State
What this run knows so far: the plan, what has been done, what failed, what is waiting on a person. Scoped to the run and discarded with it.
How it goes wrongAn agent that resumes by trusting a process that happened to stay alive. A run that resumes reads its state from the record.
Memory
What persists across runs, deliberately and selectively: preferences, prior decisions, entity facts. A data store with a retention policy and a named owner.
How it goes wrongAn accumulating transcript. It remembers something it should have forgotten, or something that was never true, and nobody can say which run put it there.
Authority
What this agent may do at all, independent of what it is asked: data classes, systems, value limits, and who is allowed to widen them.
How it goes wrongAuthority written into the prompt, where it can be argued with. It is held outside the model, and checked at step 04 every run.
Execution trace
One run, including the part where it is told no.
The reference flow worked through a concrete case rather than a client’s run. Findings from an engagement stay with the client. No timings, scores or amounts: this is a page about what gets recorded, and a trace decorated with plausible numbers would be exactly the invented evidence we refuse everywhere else.
Workflow Automation Agents
Accounts payable exception agent
Authority: Scoped to resolve invoices blocked in matching. May read purchase orders, goods receipts and supplier records; may correct a coding error; may release a payment below its value limit. May not issue credit notes, and may not change supplier bank details under any circumstances.
- 01 Trigger / intent
Queue event: invoice blocked, quantity mismatch. Requester resolved to the AP clerk who owns the queue. Intent matched to a scoped task.
OK - 02 Context
Retrieved the invoice, the purchase order, the goods-receipt note and the supplier record, under the clerk’s permissions. Supplier record returned redacted: bank details are outside this agent’s data classes.
OK - 03 Reasoning
Plan: the receipt is short against the order. Either the invoice is wrong or the delivery was partial. Proposed reading the delivery note, then correcting the line quantity, then releasing payment for the received amount.
OK - 04 Policy
Plan also proposed issuing a credit note for the difference. Refused: credit notes are outside this agent’s authority. Plan revised to raise an exception for a person instead of stopping the run.
REFUSED - 05 Tool selection
Selected the ERP line-correction tool from the registry. Inputs validated against its schema: invoice id, line, corrected quantity, reason code.
OK - 06 Human approval
Payment release exceeds the agent’s value limit, so the run paused. The approver was shown the mismatch, the correction and the refused credit note. Approved by the AP manager.
HELD - 07 Action
Line quantity corrected and payment released for the received amount, against the ERP, under an identity traceable to this run. The credit-note exception was routed to the AP manager’s queue.
OK - 08 Verification
Read the invoice back: the line matches the goods receipt, the block is cleared, the payment is scheduled. The exception exists in the queue it was routed to.
OK - 09 Audit evidence
Trace retained against the register entry for this agent: intent, sources and versions read, plan, the refusal and its rule, the approval and what the approver saw, the calls made, the verification result.
OK - 10 Learning
The refused step became an evaluation case: an agent that proposes a credit note must be refused, every run, whatever the phrasing. The approval delay was logged as a candidate for raising the value limit: a decision for the agent’s owner, not for the agent.
OK
Video · 3 min
Four specialist agents, one governed decision.
An illustrative launch question split across four specialist agents (opportunity, feasibility, capacity and governance), each reading only its own scope of one governed context. Their findings conflict, the orchestrator reconciles them into a conditional go, and a policy gate decides for each proposed action whether it runs, waits for a manager or needs an executive.
Multi-agent orchestration · 3:01
Read the video as text
- The request. In a client services workspace, the head of client services asks: “Can we launch our new AI-enabled client advisory service this quarter?” The request is routed to the orchestrator. “One business question. Many dimensions.”
- The multi-agent orchestrator. Intent: a client service launch assessment. Objective: determine whether the service can launch this quarter. Decision required: go, conditional go or hold. Four specialist agents are assigned a question each. Opportunity discovery: is there real client demand and value? Solution feasibility: can it actually be engineered? Capacity and skills: do we have the people to deliver it? Governance and risk: can it launch within policy? “One objective. Multiple specialist capabilities.”
- Specialist analysis, the four agents running in parallel. Opportunity discovery weighs client demand, business priority, use-case value and adoption signals, and reports a strong demand signal. Solution feasibility weighs architecture fit, required integrations, data availability and platform readiness: feasible, with one integration dependency. Capacity and skills weighs team availability, AI engineering skills, architecture capacity and implementation bandwidth: specialist capacity is constrained. Governance and risk weighs data sensitivity, security controls, AI policy and approval requirements: conditional approval required. “Specialised analysis. Shared enterprise objective.”
- Shared governed context. One governed context fabric (CRM and client context, contracts, knowledge, architecture, data platforms, the project portfolio, and skills and capacity), with each agent scoped to its part: commercial and client for opportunity, architecture and data for feasibility, capability and resources for capacity, policy and security for governance. One source of truth, scoped by role, with traceable evidence. “Shared context. Scoped access. Common evidence.”
- Coordination and conflict. The findings pull different ways: proceed on strong client demand; proceed with a dependency, because integration is required; a constraint, because an AI architect is unavailable for four weeks; a condition, because data approval is required before deployment. The orchestrator reconciles them (integration before launch, the architect free in four weeks, design now and deploy after the gates, data approval gating deployment), and the open gates resolve to a conditional go. “Multi-agent systems must resolve dependencies — not simply exchange messages.”
- Synthesis. One decision from four agent perspectives: conditional go. The business case is strong, technical feasibility viable, specialist capacity the constraint and data approval the governance requirement, with high confidence from evidence across the agents. The plan: begin solution design now, complete the integration assessment, obtain governance approval, confirm specialist capacity, and target a controlled launch once the gates clear. “Different perspectives. One evidence-backed decision.”
- Controlled action. The orchestrator proposes five actions and a policy gate decides each one. Creating the solution assessment, opening the integration work item and requesting a governance review are permitted, and run autonomously. Reserving specialist capacity is supervised and waits for a manager’s approval. Committing the client launch date is restricted and needs an executive’s approval. “Collaboration can be autonomous. Authority remains governed.”
- The whole system: a business objective; the orchestrator coordinating and governing four agents (opportunity, feasibility, capacity and governance); governed context, shared controls and an evidence trace beneath them; a synthesised decision; a controlled action. “Multi-agent AI is not more agents. It is specialised intelligence working as one governed system.”
Enterprise context engineering
Context is engineered, not uploaded.
An agent is only as useful as what it is allowed to know, and that context is engineered rather than retrieved. What an enterprise agent reasons over:
- Governed enterprise records
- Approved document sources
- Semantic retrieval
- Tool and API results
- Workflow state
- Caller identity
- Permission scope
- Provenance
- Conversation history
- Business rules
Architecture & operating model
The pieces that must exist.
Reference architecture
- Knowledge source
- Permission layer
- Tool / API registry
- Agent orchestration
- Human approval
- Evaluation harness
- Audit log
- Monitoring dashboard
Operating model
- Agent owner
- Authority boundaries
- Approved tools
- Approved data sources
- Policy checks
- Escalation rules
- Evaluation threshold
- Traceability
- Audit evidence
- Monitoring owner
- Incident response
Agent architecture
Knowledge sourcesystems of record · documents
Permission layerthe caller’s access, not the agent’s
Agent orchestrationreasoning · tool selection
Tool / API registryapproved tools only
Human approvalwhere the cost of being wrong is high
Action, loggedaudit log · traceability
EVALUATION HARNESS · AUTHORITY BOUNDARIES · POLICY CHECKS · MONITORING
Agent families
The agents enterprises actually deploy.
Each family is a role, not a chatbot: scoped to a workflow, a set of approved tools, and a defined escalation path.
Workflow Automation Agents
Agents that execute multi-step enterprise workflows with approvals and system integration.
Compliance & Evidence Agents
Agents that collect, summarise, and organise compliance evidence for governance and audit workflows.
Engineering Productivity Agents
Agents that support requirements, code analysis, test generation, defect triage, and release readiness.
Quality Engineering Agents
Agents that generate tests, analyse coverage, support regression, and improve release confidence.
Customer Operations Agents
Agents that support service teams with knowledge retrieval, triage, summarisation, and next-best action.
Executive Intelligence Agents
Agents that summarise business performance, risk signals, programme progress, and decision options.
Use cases
Where this lands first.
- Answers to enterprise knowledge questions, drawn from governed internal sources
- Multi-step workflows with approvals, such as resolving invoices blocked in matching
- Compliance evidence collected, summarised and organised for audit
- Requirements support, defect triage and release readiness
- Test generation, coverage analysis and regression support
- Service triage, summarisation and next-best action for customer operations
- Summaries of performance, risk signals and programme progress for leadership
Delivery lifecycle
How an agent gets to production.
Discovery through operation, with evaluation and approvals designed in, not retrofitted. This is the build, once; the execution lifecycle above is what the agent does on every run.
Discover workflow
Design agent role
Connect tools & knowledge
Add human approvals
Evaluate behaviour
Deploy securely
Monitor & improve
Engagement packages
Ways to start.
Scoped entry points, each ending in something you can act on.
2-week workflow discovery
Map the workflow, systems, data, and decision points an agent must respect.
4-week agent design sprint
Design the agent role, tools, approvals, evaluation plan, and reference architecture.
8–12 week production pilot
Build, evaluate, and deploy the agent into a governed production workflow.
Ongoing AgentOps & evaluation
Monitor behaviour, run evaluations, and improve the agent in operation.
What makes this different
Non-negotiables in our builds.
- Tool use with guardrails
- Permission-aware knowledge access
- Human-in-the-loop controls
- Evaluation before release
- Governance and auditability
- Observability after deployment
- A scaling model, not a pilot: the second agent inherits the context layer, evaluation harness and controls built for the first
Evidence
What exists to look at.
What this evidence is
No client agent deployment is published. These are the reference architecture and the evaluation framework agents are built and tested on.
- 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
Permission-aware retrieval and grounding for the knowledge agents act on.
- VeriCore AI Evaluation Studio →LLM, RAG & AGENT EVALUATION
Agent task evaluation: task completion, tool selection and instruction adherence, before release and after it.
Request an Architecture Walkthrough.
Tell us the workflow, the systems, and the constraints, and we will come back with a focused next step.