CUSTOM AI AGENT DEVELOPMENT
Custom AI agent development
A demo agent proves it can work once. A production agent has to work the thousandth time.
- Purpose-built and integrated with your systems of record through governed connectors
- Least-privilege tool access, with consequential actions gated behind approval
- A built-in evaluation harness that runs in CI, not a one-time manual check
An agent you cannot evaluate is an agent you cannot improve or trust.
A chatbot answers. An agent acts, it plans, calls your tools, and updates systems of record.
That capability is exactly why an agent needs governed access, continuous evaluation, and approval gates a chatbot never does. We watch for the failure that hides best: error compounding, where a 95%-reliable step is only about 60% reliable over ten. That is why we decompose long tasks into checkpointed stages with validation between them rather than one long, brittle chain. Development picks up the shortlist from Agentic AI Discovery and Process Mining and the blueprint from Multi-Agent System Design, then builds within the limits your risk team will accept.
We build the smallest capable agent, then prove it on the case nobody anticipated.
The same core, sized to the agent's job.
- A purpose-built agent integrated with your systems of record through governed connectors.
- Governed tool and function calling: least-privilege access, with consequential actions gated behind approval.
- Memory that fits the task: short-term scratchpad, long-term vector retrieval, and episodic or semantic memory where the work needs it.
- A planning strategy matched to the problem, ReAct for reactive tool use, plan-and-execute for longer-horizon tasks.
- State persistence and checkpointing, so a failed step resumes instead of restarting.
- A built-in evaluation harness: trajectory and step-level tests that run in CI, not a one-time manual check.
- Guardrails and budgets: input and output checks, step limits, and circuit-breakers against runaway loops.
Every change is measured, because evaluation is the weakest link in most agent programs.
We treat evaluation as core engineering, not a demo you run once and hope holds.
Define done right
Set the checkable success criteria with a domain SME, the standard the agent is measured against. Success is defined before a line is built.
Build the smallest capable agent
The tools, memory, and planning it genuinely needs, and nothing more. Scope is the first safety control.
Run trajectory evals
Tool-selection accuracy, task completion, and cost and latency per task, on a representative case set. Measured on cases, not on a good demo.
Diagnose the trajectory
Where did it pick the wrong tool, loop, or lose context, not just whether the answer was right. Fix the path, not only the output.
Harden and gate
Add guardrails, approval gates, and budgets where the evals expose real risk. Controls are placed where evidence points.
Regression-test every change
A prompt tweak cannot silently break a case that used to pass. Quality holds as the agent evolves.
Five steps, from a scoped job to an agent wired into CI.
Scope
Define the agent's job, tools, and success criteria with your domain experts.
Build
The smallest capable agent, built against a representative case set.
Evaluate
Trajectory measured continuously, tool selection, completion, cost, and latency.
Harden
Guardrails, approval gates, budgets, and checkpointing where the evals expose risk.
Deploy and hand over
Onto your runtime, with evals wired into CI, or run it with Governance and Risk Management in place.
Chosen for production control, not demo speed.
A representative stack by layer. We often prototype fast in CrewAI to validate a role structure, then build the production agent where checkpointing and human-in-the-loop control matter.
We work across cloud and model providers, and are not locked to one framework. Figures are placeholders; Softobiz to verify against your environment.
From a promising demo to an agent that carries real load.
Challenge: Equalis Group had an agent that demoed well but failed on edge cases, with no way to tell why or to stop it regressing.
Result: An evaluated, governed agent handling [XX%] of the target process, with a trajectory eval suite in CI on every change. (Softobiz to verify.)
Custom development sits at the center of the Agentic AI practice.
Agentic AI
The parent practice this build capability belongs to.
Discovery and Process Mining
The shortlist of processes worth building an agent for.
Multi-Agent System Design
The architecture custom development builds against.
Platform Architecture and Deployment
The runtime the finished agent deploys onto.
Governance and Risk Management
The autonomy line, approval gates, and audit the agent runs inside.
AI Enablement and Change Adoption
The role redesign that turns a working agent into an adopted one.
What engineering leaders ask us first.
A chatbot answers. An agent acts, it plans, calls your tools, updates systems of record, and completes multi-step work. That capability is exactly why it needs governed access, evaluation, and approval gates a chatbot never does.
Whichever fits the reliability and integration needs, LangGraph and Semantic Kernel for production-grade control, CrewAI where a fast role-based prototype helps. We are not locked to one.
We measure the full trajectory against a representative case set, tool-selection accuracy, task completion, cost, and latency per task, and gate launch on those results, not on a good demo.
Take one scoped process and build an evaluated, governed agent that carries real load.
Governed tools, task-fit memory, and trajectory evals in CI, so the agent works the thousandth time, not just the first.