Practice i of v · AI agents and automation
Agents that act. People who decide.
Production agents that read context, make calls and execute real work inside your systems. Every consequential action waits at an approval gate a named person owns, and every decision leaves a record.
01 The problem we solve
Autonomy without accountability does not reach production.
Most agent work stalls in the same three places. Each one is an engineering problem, and each one has an engineering answer.
Rules that break on the edge case
Rule-based workflows and hand-offs handle the common path and fail on everything else. The exceptions land back on your team, usually at the worst moment.
A demo that cannot survive traffic
A pilot that works on ten examples is not a system. Without evaluation, monitoring and a way to roll back, it never leaves the sandbox.
Nobody can say who approved it
Risk and compliance teams block agents they cannot audit. If an action cannot be traced to a named person and a recorded reason, it should not run.
02 What we deliver
Four classes of agent, one team.
Each class serves a different kind of work, and most enterprises need more than one. Every agent ships with the same controls: an approval-gate map, an append-only audit log, evaluations and production monitoring.
Customer intelligence agents
Support, triage and resolution at enterprise scale. Agents that understand context, resolve what they are allowed to and escalate the rest to the right person.
- Support automation
- Intent detection
- Escalation logic
Claude, GPT-4, function calling
Data and research agents
Retrieval, synthesis and reporting pipelines, connected to your knowledge bases, databases and APIs so the answer reflects what is true today.
- RAG pipelines
- Data synthesis
- Automated reporting
RAG, vector databases, Pinecone
Workflow automation agents
Process orchestration, approvals and hand-offs. Agents that adapt to context and handle edge cases, in place of brittle rule-based systems.
- Process orchestration
- Approval flows
- System integration
Custom APIs, event pipelines, webhooks
Multi-agent systems
Orchestrator-and-specialist networks. Complex tasks decomposed, delegated and completed by coordinated agents, with one accountable for the result.
- Orchestration
- Delegation
- Coordination
CrewAI, LangGraph, LangChain
Model-agnostic by design. We implement on CrewAI and LangGraph and integrate Claude, GPT-4 and open-weight models depending on latency, cost and data-residency constraints, and we can swap a model without rewriting your system.
Reading Fig. 1
One agent is accountable for the result. Specialists do the parallel work, their output converges into a single proposal, and nothing consequential reaches your systems until the person who owns that class of action approves it.
03 Human in command
The agent proposes. The gate decides. The record stays.
Approval gates
Every proposal waits for a person.
During architecture we classify every action an agent can take. Consequential classes, such as anything that moves money, changes a customer record or cannot be undone, get a named owner and a gate. Everything else runs automatically and is still logged.
The queue is the control surface your operations and risk teams use. The log is what your auditor reads. Both are built in from the first release.
04 How an engagement runs
From brief to production in increments.
We map the use case, the data and what success has to look like before writing a line of code. You can stop at the end of any step.
- 01
Define
Weeks 1 to 2 · fixed feeArchitecture assessment: use cases, data sources, success criteria, build-versus-buy and a written ROI case.
- 02
Architect
Weeks 2 to 4Models, tools, memory and guardrails, and the approval-gate map: which actions are consequential, and who owns each one.
- 03
Build and deploy
Week 4 onward · minimum three monthsA dedicated senior team ships agents to production in increments, with evaluations and monitoring from the first release.
- 04
Run
OngoingManaged operations: monitoring, evaluation, incident response and continuous improvement, by the team that built it.
05 · Case study, agentic workflow platform
$1.2M of development cost, eliminated.
Enterprise teams needed to chain API calls and database actions without waiting on IT. Algobain built a visual, agentic workflow platform now used by more than 150 organisations.
All case studies
The brief
Long development cycles for simple internal tools, and business users dependent on IT for every workflow change. Client name withheld under NDA.
What we built
A node-based workflow engine with triggers from webhooks, schedules and events; an action library for email, SMS, API calls and database operations; conditional logic, retries and run-level debugging.
What it runs on
Containerised with Docker and orchestrated on Kubernetes, multi-tenant, with CI/CD and monitoring built into the platform rather than added later.
Client view, another engagement
“They automated the work our support team dreaded, and left us in command of every decision that actually mattered.”
06 · Insights
Agents act on the systems you already have.
An agent can only be as dependable as what it calls. These pieces cover the work that usually comes first.
07 Questions
What teams ask before they commit.
- Which actions need a human approval?
- The ones you classify as consequential during the architecture phase. Moving money, changing a customer record, contacting a customer or anything that cannot be undone are the usual starting points. Each class gets a named owner. Everything else runs automatically and is still logged.
- Which models and frameworks do you use?
- Whatever the problem calls for. We implement on CrewAI and LangGraph and integrate Claude, GPT-4 and open-weight models, chosen on latency, cost and data-residency constraints. A model can be swapped without rewriting the system around it.
- Can agents work with our existing systems?
- Yes, through the APIs and events those systems already expose. Where a legacy system has no clean interface, our legacy modernization practice opens one at the seam first.
- What happens when an agent gets it wrong?
- A declined proposal is logged and returned to the agent, not silently retried. Sampled runs are scored against an evaluation rubric, and incidents are handled under managed operations by the team that built the agent.
- Who runs the agents after launch?
- We do, unless you would rather take them in-house. Managed operations covers monitoring, evaluation, incident response and improvement, with 99.7% uptime across the production AI systems we operate today.
- How is our data handled?
- NDA first, before you share any detail. GDPR-compliant delivery, SOC 2-aligned practices (aligned, not certified) and a DPA on request for EU and UK entities. See Trust.
Related practices
What the agents stand on.
Put your first agent under a gate.
Tell us the work you want an agent to take on. We will map the use case, the right architecture and the approvals it needs.
- 1Within 48 hoursA senior engineer replies. If we are not the right fit, we say so.
- 2NDA, then a working sessionWe sign first, then map the work, the data and the owners.
- 3A written proposalScope and fixed fee for the two-week assessment.