Common problems
Complex processes require multiple judgment steps
A single AI agent is not specialized enough
Need automated verification and QA
How MADIAD solves it
How an agent works
Unlike plain automation, it picks its own route — inside the fence you set.
Flow diagram of an agentic workflow, six steps from intake to audit trail.
Takes the work
- 01
Read the task and its context
Takes the request along with the relevant data and works out what is actually being solved.
- Request
- History
- Background data
- 02
Plan its own approach
Which tools, in what order. This is what separates it from plain automation: there is no single predrawn path to follow.
- Tool selection
- Sequencing
- Replanning
- 03
Call real tools
Queries databases, reads files, calls APIs, writes records. The output is work done, not a paragraph of suggestions.
- Queries
- File reads
- API calls
- 04
A second agent checks the first
One gathers, another verifies. Split roles catch each other's mistakes instead of one agent going confidently wrong end to end.
- Cross-check sources
- Flag contradictions
- 05
Through the guardrails before anything leaves
Anything touching money or reaching a customer is confirmed first — even at the highest permission level. Data outside scope has no path in.
- Threshold stops
- Ask a human
- Data-tier lock
- 06
Execute and record
What was done, when, and on what data. No step is a black box to the people running it.
- Full log
- Traceable
- Replayable
Done, every step traceable
Guardrails are code running on every output, not prompt instructions — so nothing depends on the model remembering a rule.
What agentic workflows do
Unlike plain automation, they choose their own route — inside the fence you set.
Plain automation walks one predefined path. An agent reads the situation and picks its tools and order, which is what handles work whose inputs cannot all be anticipated.
Queries databases, reads files, calls APIs, writes records, sends notifications. The output is work done, not a paragraph of suggestions.
One gathers, one verifies, one synthesises. Splitting roles means they catch each other's mistakes rather than one agent confidently going wrong end to end.
What may be done, which thresholds require asking, which data is off limits — enforced by checks that run on every output, so nothing depends on the model remembering a rule.
Monitor and alert · propose and wait for a human · act within set limits. Changing level is a setting, not a rebuild.
What the agent did, when, and on what data. No step is a black box to the people running it.
What you get
- Production-ready multi-agent system
- Monitoring dashboard
- Prompt + config documentation
- Support retainer
What we need from you
- The work you want handed over, and a clear boundary: what the agent decides, what it must ask about
- Access to the data and tools it needs
- Someone to review during the supervised period
- Alert thresholds — what counts as abnormal and needs a human
What to know before you start
- Anything touching money or going out to a customer is confirmed first — even at the highest permission level.
- Agents only read data you allow. Those boundaries sit in the data layer, not in prompt instructions, so they cannot be talked around.
- Not a fit for work that must be exactly right every time with no review. That belongs in rule-based automation.
- Running costs are higher than plain automation because every step involves reasoning. Anything with clear rules should stay in plain automation — it is markedly cheaper.
- Expect a supervised period before permissions widen. Opening everything on day one is not the right start.