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Agentic pipelines that are bounded, auditable and yours.

We design and build custom agentic flow pipelines: deterministic orchestration of language-model agents, typed tools and business rules, with checkpoints where a person must approve and a trail that lets you replay any decision months later. Bespoke to your process, deployed in your environment.

Custom build Human-in-the-loop Model-agnostic Silver AI compatible
Close-up of a circuit boardAgentic pipelines
Definition

What we mean by an agentic pipeline.

An agentic pipeline is a workflow in which language-model agents do the reading, drafting and classifying, while a deterministic orchestrator decides which agent runs, with which tools, in what order, and under what constraints. The orchestrator is ordinary software: it holds state, enforces rules, records every step and stops at defined checkpoints for human review.

This is a deliberate contrast with a single chat interface or an unconstrained autonomous agent. The model contributes judgement on unstructured inputs; the pipeline contributes control. When a step fails, the orchestrator retries, escalates or falls back to a person according to rules you approved in advance, not according to what the model improvised.

The result is a system that a compliance officer can read, an internal auditor can replay, and an operations team can run without a data scientist on call.

Platform choice. Pipelines can be built on Silver AI, our enterprise agent platform, or on orchestration tooling you already operate. The contracts, evals and audit trail described here are required in either case.

Anatomy

Five stages, every run.

Whatever the domain, a Silverline pipeline moves through the same five stages. Each stage emits structured events to the audit store, so a run can be reconstructed step by step.

Stage 1
Trigger & ingest

A file arrival, queue message, schedule or API call starts the run. Inputs are validated, classified and normalised into typed records before any model is invoked.

Stage 2
Plan

A planning agent proposes the sequence of steps and tools needed for this specific case, within a bounded set of allowed actions. The plan is stored before execution.

Stage 3
Tool execution

Agents call typed tools: database queries, core-system APIs, document parsers, calculators. Each call is validated against its schema and its result is logged.

Stage 4
Verification & human approval

Outputs are checked by independent verifier agents and rules. Cases above a risk or value threshold are routed to a named approver with the full evidence pack.

Stage 5
Audit & learning

The complete run, including prompts, tool calls, model versions and approvals, is written to an immutable store. Corrections feed the evaluation suite.

Design principles

Constraints that make a pipeline fit for a regulated enterprise.

These six principles are non-negotiable in our builds. They add engineering effort up front and remove most of the operational risk later.

Bounded autonomy

Agents operate within an explicit action set, budget and scope. They cannot invent new tools, widen their own permissions or act outside the case they were given. Autonomy is a configuration, reviewed like any other change.

Typed tool contracts

Every tool an agent may call has a versioned schema for inputs, outputs and errors, plus a declared side-effect class (read, write, irreversible). Contracts are tested independently of any model.

Evaluation harness before release

A suite of real, anonymised cases with expected outcomes is assembled during discovery. No release candidate reaches production until it clears accuracy, safety and cost thresholds on that suite.

Replayable audit trail

Prompts, retrieved context, tool calls, model identifiers and human approvals are recorded for each run. Any decision can be reconstructed and explained to an auditor or regulator on request.

Fallbacks to humans

Low confidence, schema violations, unusual values or policy triggers route the case to a named person with the evidence assembled. The pipeline degrades to assisted processing, never to silent failure.

Cost and latency budgets

Each stage has a token, call and time budget enforced by the orchestrator. Budgets make unit economics predictable and stop a misbehaving run from consuming the month's allocation.

Delivery artefacts

What you receive, and when.

Every engagement produces the same five documents. They are written for your architecture board, your risk function and your operations team, and they remain your property.

ArtefactWhat it containsDeliveredPrimary reader
Discovery memoCurrent-state process map, volumes and exception rates, data inventory, candidate scope, risk register and a go/no-go recommendation.Week 3Sponsor, steering committee
Agent specificationRoles and responsibilities of each agent, allowed actions, confidence thresholds, escalation rules, prompts under version control and the model selection rationale.Week 6Architecture, risk and compliance
Tool catalogueEvery tool contract with schemas, side-effect class, owner, rate limits, test coverage and the systems it touches.Week 6 Updated per releaseIntegration and security teams
Evaluation suiteAnonymised test cases with expected outcomes, scoring rubric, release thresholds and the latest results for each candidate build.Week 8 Re-run monthlyQuality, model risk, internal audit
RunbookDeployment topology, monitoring dashboards, alert thresholds, incident procedures, approval queues, change control and the rollback plan.Hand-overOperations and support
Illustrative engagements

Two anonymised examples of the shape of the work.

The engagements below are illustrative, described in anonymised form to show scope, sequence and controls. Figures are indicative of the pattern rather than a specific client's results.

Reconciliation pipeline for a private-sector bank

A treasury operations team reconciled nostro and settlement accounts across several statement formats, with a large share of breaks resolved by hand. The pipeline ingests statements and ledger extracts, matches on typed rules first, and uses agents only for the unmatched residue: reading narrative fields, proposing candidate matches and drafting break explanations.

  • Every proposed match above a value threshold is queued for maker-checker approval with the evidence attached.
  • The eval suite was built from three months of historical breaks with known resolutions before any model was selected.
  • Audit store retains prompts, tool calls and approvals for the retention period set by the bank's records policy.

Collections workflow for a mid-market NBFC

A lender's collections team handled early-stage delinquencies through manual call lists and templated messages. The pipeline segments accounts nightly, drafts customer-specific communications within approved language, schedules contact according to regulatory windows, and records promises-to-pay and disputes back into the loan system.

  • Agents may draft and schedule; they may not waive charges, restructure or threaten action. Those steps are human-only.
  • Fair-practice and contact-timing rules are enforced by the orchestrator, not by the prompt.
  • Dispute or hardship signals in customer replies route the account to a named officer within the same day.

Note. Client names, volumes and outcomes are disclosed only with written consent and are not published on this site.

Bring us one process that still runs on people and spreadsheets.

A three-week discovery sprint will tell you whether an agentic pipeline is warranted, what it would cost to run and where the human checkpoints belong.