AI AGENT BUILDER
AI agent development on a shared platform
We develop shared tooling and reusable patterns for your teams to compose, test and deploy agents within your own platform.
- Scaffolding, tool registry, memory, and evaluation on your cloud
- Every agent traced, permissioned, and evaluated by default
- Templates and runbooks, so teams ship without us in the loop
An AI agent builder should reduce platform sprawl.
Instead of each team reinventing orchestration, memory, evaluation, and deployment, they compose on one opinionated toolkit.
The shared foundation shortens setup, applies consistent controls and makes operating cost visible. It does not replace the teams building agents. It gives them reusable scaffolding and a governed catalogue, so their effort goes into the use case rather than rebuilding platform services.
A common way to build agents: faster to ship, consistent to govern.
A complete, opinionated toolkit on one foundation.
Agent scaffolding
Templates for common patterns: single-agent, supervisor, multi-agent crews. A new agent starts from an established, reusable skeleton.
Tool and connector registry
A governed catalogue of tools, APIs and data sources agents can call. Access control per team, not per hand-rolled agent.
Memory and state
Short- and long-term memory handled by the platform. Not re-implemented per agent.
Orchestration runtime
Reliable multi-step execution with retries, timeouts, and human-in-the-loop checkpoints. Explicit control flow, not emergent chaos.
Evaluation and testing
An agent eval harness for trajectories and outcomes. You can tell if a change regressed behaviour.
Guardrails and policy
Permission boundaries, action approvals, and safety checks in the execution path. Consequential actions gate to approval.
Deployment and observability
One-path deploy with full tracing of every step, tool call, token, and cost. Nothing ships as a black box.
Cloud-native on your platform.
Tool-pragmatic across the agent ecosystem, built cloud-native on your platform of choice.
Not every agent needs a custom build.
And not every packaged tool will fit your governance. We help you draw the line.
Standard task, meets your rules
A packaged agent covers a commodity task and meets your security and data rules.
Proprietary and governed
The workflow is proprietary, touches sensitive data, or needs deep integration and evaluation you must control.
Buy commodity, build the core
Buy for commodity steps; build the differentiated, governed core on your foundation.
The trap is building everything, or buying everything. The right answer is usually a governed platform for what differentiates you, and adoption for what does not.
Prove the demand, then pave the road.
Assess the demand
Confirm multiple agent use cases justify shared tooling over one-off builds.
Stand up the foundation
Scaffolding, tool registry, memory, and evaluation on your cloud.
Build the first agents on it
Prove the patterns with real workflows, not samples.
Add guardrails and observability
Permission boundaries, approvals, tracing, and cost.
Enable your teams
Templates and runbooks, so teams ship agents without us in the loop.
From scattered experiments to a repeatable capability.
[CLIENT / INDUSTRY] · ENTERPRISE AI AGENTS[XX%] faster
When the plumbing became shared, the agents multiplied.: A [client] had teams building agents ten different ways, each reinventing orchestration and evaluation. We stood up scaffolding, a governed tool registry, shared memory, and an eval harness, then proved the patterns on real workflows before enabling teams to ship on their own. (Softobiz to verify.)
The composition layer of the platform.
The Agent Builder sits inside Scaled GenAI and AI Platforms and powers our Agentic AI practice.
AI Platform Design and Implementation
The platform the Agent Builder runs on.
LLMOps
Evaluation and cost control for the agents you ship.
LLM Fine-Tuning
Adapt the models your agents call for form and behaviour.
MLOps
The discipline for classical and predictive models.
GenAI OS
The shared services that host agentic workloads org-wide.
Dedicated Teams
A team to own and evolve the Agent Builder with you.
What platform leads ask us first.
The Agentic AI practice designs and delivers specific agent systems. The Agent Builder is the shared foundation those agents are built on: tooling, patterns, and guardrails reused across teams.
Both, deliberately. Buy for commodity tasks that meet your security rules; build on your own foundation for proprietary, data-sensitive, or deeply integrated workflows you need to govern.
Through an agent evaluation harness that scores trajectories and outcomes on real cases, so a prompt or tool change cannot silently break behaviour. It is the same discipline we bring in LLMOps.
Shared agent tooling, or one-off builds per team?
Let's assess whether shared tooling beats one-off builds for your teams, and stand up the foundation if it does.