01
Prepare automatically
For many workflows, AI can research, gather evidence, compare information, and prepare a recommendation without creating irreversible consequences.
AI Agents & Workflow Automation
AI agents are useful when a workflow requires more than generating text — when the system needs to gather information, make decisions, use tools, interact with software, and move a task forward.
Relytic designs and builds controlled AI agents for business workflows where reliability, traceability, and human oversight matter.
The problem
When manual workflows become the bottleneck
Many business processes still depend on people repeatedly doing the same sequence of work.
The recurring work:
- Search for information across several systems
- Read and compare documents
- Copy data between tools
- Check requirements or policies
- Prepare drafts, summaries, or recommendations
- Wait for a person to approve the next step
- Repeat the same process for every case
A useful agent should know what it is allowed to do, what evidence it needs, what should be validated, and when a human needs to take over.
What we build
Document and research agents
Agents that search internal knowledge, retrieve relevant evidence, analyze documents, and produce structured outputs for people to review.
Business workflow automation
Automate repeatable multi-step workflows across internal systems, APIs, databases, and documents.
Human-in-the-loop agents
Keep important decisions under human control while allowing AI to prepare the evidence, recommendation, or action for approval.
Compliance and review workflows
Collect evidence, compare information against defined requirements, flag missing information, and prepare cases for expert review.
Internal operations agents
Automate repetitive work such as information gathering, triage, drafting, classification, and moving information between systems.
Tool-using assistants
Give AI controlled access to the software and APIs needed to complete a real task rather than stopping at a conversational response.
System anatomy
We build the workflow, not just the agent loop
A production agent can involve much more than an LLM and a collection of tools. Depending on the project, the system may include:
- Workflow and state management
- Retrieval from internal knowledge
- Tool and API integrations
- Structured inputs and outputs
- Deterministic validation rules
- Permissions and access controls
- Human approval steps
- Retry and recovery logic
- Audit trails and source traceability
- Model routing
- Cost and latency controls
- Monitoring and evaluation
The architecture should match the risk and complexity of the workflow. A low-risk drafting assistant and an agent capable of changing business records should not be engineered in the same way.
Controlled execution
Reliability means controlling what the agent can do
Agent reliability is not only about whether the model gives a good answer.
We evaluate the complete workflow.
ObservePlanUse toolsValidateApproveActRecover
- Task success
- Did the agent actually complete the intended task correctly?
- Tool use
- Did it select the right tool, provide valid arguments, and interpret the result correctly?
- Evidence and validation
- Did the agent use the right documents or system state, and can important outputs be checked with rules, schemas, or a second verification step?
- Human escalation
- Does the system recognize when it should stop and ask for review rather than continuing autonomously?
- Failure recovery
- What happens if an API fails, a tool returns incomplete information, an output is invalid, or the workflow is interrupted?
- Production behavior
- We also measure latency, cost, failure rates, unnecessary escalations, and other operational signals that affect real users.
Evidence
Autonomy should match the risk
The goal is not maximum autonomy. The goal is the right level of automation for the business process.
02
Approve important actions
Financial, legal, customer-facing, or irreversible actions can remain under human control while the agent handles the repetitive preparation.
03
Automate lower-risk work
When consequences are limited and outputs can be validated reliably, more of the workflow can run automatically.
Common use cases
Tender and proposal workflows
Collect requirements, retrieve supporting company evidence, identify gaps, and prepare material for human review.
Compliance and document review
Analyze documents against defined requirements, flag missing or conflicting information, and route uncertain cases to an expert.
Research agents
Search multiple internal or external sources, synthesize evidence, and produce structured research with traceable sources.
Internal operations
Handle repetitive information gathering, classification, routing, drafting, and system updates across teams.
Support workflows
Retrieve the right knowledge, analyze a case, prepare a response, and escalate situations that require human judgment.
Data and reporting workflows
Gather information from tools and databases, validate it, perform analysis, and prepare recurring outputs.
Honest advice
When an agent is — and is not — the right solution
Agents are useful when the workflow genuinely requires multiple steps, changing state, tool use, or decisions based on intermediate results.
They are unnecessary when the task can be solved more reliably with a simple API call, deterministic workflow, search system, document extraction pipeline, or single model request.
We choose the simplest architecture that can solve the problem reliably.
How we work
From workflow to production
- 01
Map the process
We identify the current workflow, systems involved, decision points, manual effort, failure risks, and where human judgment is required.
- 02
Define control and success
We decide what the agent may do autonomously, what requires approval, and how successful task completion will be measured.
- 03
Build and evaluate
We implement the tools, workflow logic, model behavior, validation, and recovery paths and test them against representative cases.
- 04
Integrate and deploy
We connect the agent to the real systems and introduce it into the workflow with the appropriate permissions and safeguards.
- 05
Monitor and improve
We track failures, escalations, user corrections, cost, latency, and task success as the system operates in production.
- 01
Map the process
We identify the current workflow, systems involved, decision points, manual effort, failure risks, and where human judgment is required.
- 02
Define control and success
We decide what the agent may do autonomously, what requires approval, and how successful task completion will be measured.
- 03
Build and evaluate
We implement the tools, workflow logic, model behavior, validation, and recovery paths and test them against representative cases.
- 04
Integrate and deploy
We connect the agent to the real systems and introduce it into the workflow with the appropriate permissions and safeguards.
- 05
Monitor and improve
We track failures, escalations, user corrections, cost, latency, and task success as the system operates in production.
Next step
Have a workflow that still requires too much manual work?
Book a 30-minute conversation with Relytic to walk through the process, the systems involved, and where controlled AI automation could remove repetitive work.