AI Readiness Assessment
Find what must change before an agent belongs in your workflow. Review the data, ownership, controls and operational dependencies together.
Services for AI agents
Start with the agentic AI decision. Shape the work around it.
These services address AI agents and the wider systems in which they operate—not AI in the abstract. Ten focused service areas connect investment decisions, operating change, harness engineering and the evidence needed to manage AI agents responsibly.
Find your starting point
Where are you in the journey?
AI Strategy
Prioritise opportunities against business value, readiness and constraints.
AI Transformation
Plan adoption, ownership and workflow change around a bounded business objective.
AI Transformation Sprint
Move from an interesting use case to a deliberate adoption decision. Connect workflow value, organisational readiness and the changes needed to take the next step.
AI Agentic Transformation Blueprint
Turn agentic ambition into an operating plan. Connect workflow choices, architecture, people and controls to explicit decision points.
AI Executive Agentic AI Academy
Give leaders a practical language for agentic decisions: where to delegate, what to oversee and which evidence to ask for.
AI Fractional Agentic AI Office
Create a regular place for agentic AI decisions. Connect initiative priorities, architecture questions, risk ownership and evidence reviews.
AI Fractional Agentic Transformation Office
Coordinate the operating changes behind agent adoption. Align workflow redesign, ownership, dependencies and adoption measures.
AI Harness Engineering
Engineer the execution harness that connects AI models to tools, context, orchestration and recovery.
AI Harness Engineering Pod
Engineer a bounded AI execution harness with tool adapters, context handling, orchestration, recovery and observable traces.
AI Harness Integration Pod
Integrate an AI harness into existing systems with bounded tool access, context interfaces, failure handling and ownership handover.
AI Evaluation
Build repeatable evidence about agent behaviour, components and workflow reliability.
AI Agent Evaluation Evidence Diagnostic
Find the gaps between your release decision and the evidence behind it. Start with one agent workflow and a bounded set of questions.
AI Agent Evaluation Foundation
Turn scattered tests into a repeatable release discipline. Connect the behaviours that matter to datasets, evaluation criteria and evidence your team can maintain.
AI Agent Component Evaluation
Find where agent behaviour breaks down. Follow memory, retrieval and delegation through your workflow, then isolate the components that need attention.
AI Agent Protocol Interoperability Lab
Find where connected agents disagree. Exercise message contracts, identity propagation and failure recovery across your selected integrations.
AI Retrieval Evaluation
Find why the right evidence is missing from an agent’s answer. Evaluate retrieval, grounding and context assembly against representative tasks.
AI Long-Horizon Agent Evaluation
Examine what changes when an agent keeps working. Evaluate accumulated state, recovery and goal adherence across extended tasks.
AI Orchestration Reliability Review
Follow the work across agents and tools. Examine routing, delegation, retries and recovery where coordination can fail.
AI Assurance
Review the evidence supporting continued use as agent behaviour, dependencies and operating conditions change.
Agent Release Readiness Review
One agent workflow. Evidence your release board can sign. A fixed-scope review of what the agent does, what it may do and what happens when it is wrong.
AI Agent Memory Assurance
Examine what your agent remembers, when it retrieves it and whose information it can expose—in the deployment where it actually works.
AI Continuous Agent Assurance
Keep evidence aligned with change. Define recurring checks for the models, prompts, tools and workflows your agents depend on.
AI Production Agent Assurance
Review the evidence needed to operate a specific agent workflow, from release criteria to monitoring, escalation and rollback.
AI Security and Privacy
Examine agent authority, adversarial behaviour, isolation boundaries and personal-data flows in an agreed threat and processing context.
AI Agent Authorisation Review
Make delegated authority inspectable. Trace who an agent acts for, what it can access and how that access can be withdrawn.
AI Agent Runtime Security Review
Make every delegation explicit. Examine who the agent acts for, where it can execute and which tools it can reach when instructions alone are not enough.
AI Agent Red Team
Challenge the boundaries that matter to your workflow. Test agreed attack scenarios against agent instructions, tools and data access.
AI Agent Sandbox Assurance
Examine the boundary between agent execution and the systems around it, including files, networks, secrets and cleanup.
AI Secure Agent Architecture Review
Design authority and isolation into the system. Review trust boundaries, credentials, tools and recovery before deployment choices become difficult to change.
AI Secure Agent Deployment Review
Look beyond a successful demonstration. Examine the permissions, dependencies and operational controls that shape an agent’s behaviour in deployment.
AI Privacy Engineering Review
Trace personal data through your agent workflow. Examine collection, memory, retrieval, tool access and retention against your agreed requirements.
AI Governance
Design policies, decision rights and oversight routines that fit the way AI is used.
AI Impact Measurement
Define baselines and measures for outcomes, costs, quality and attributable change.
AI Product-Market Fit
Research the buyer, the workflow and the evidence behind an AI product decision.
Explore this area to discuss the scope of your initiative.
Security Engineering
Engineer secure architecture, delivery controls, embedded products and release evidence.
Explore this area to discuss the scope of your initiative.
Cryptographic Engineering
Assess cryptographic architecture, key and certificate lifecycles, and post-quantum migration.
Explore this area to discuss the scope of your initiative.
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From a question to a scope
Agree the work before committing to it.
A scoped agentic AI engagement connects a decision to a usable output
- Frame the agentic decision
Bring one workflow, its intended level of autonomy, an accountable owner and the uncertainty holding up progress.
- Agree the system boundary
Confirm the agents, models, tools, data, people, inputs, exclusions and evidence in scope.
- Define the decision output
Specify the artifact, review criteria and next decision the engagement should support.
Behind the work
Explore the disciplines behind each decision.
Evaluation, identity, security, architecture and organisational change meet inside real workflows. Explore the capability map and how these areas connect.
Explore capabilitiesPrefer to start with a decision?
The outcome guides connect services across these families.
A little more clarity
Questions worth asking.
Start with the questions that shape a better decision.
What is the most focused way to start working with Softlander?
Start with the decision holding up your team. An assessment suits an unclear starting point; a defined project or engineering engagement suits a build; a scoped evaluation suits a release question. Our How we work page explains these formats and the responsibilities agreed before work starts.
What should an evaluation engagement leave our team with?
Agree outputs against your decision: the workflow boundary, test specification, observed results, findings and prioritised next steps. Scope depends on authorised inputs, representative cases and the review criteria agreed with your team.
Can Softlander review security and evaluation together?
Yes, when the scope requires it. Task quality alone cannot answer whether an agent used the correct identity, stayed within authority, handled tool uncertainty or recovered safely. Those requirements can be traced through one review while specialist boundaries remain explicit.
What work is outside Softlander’s current service boundary?
The published service direction does not promise certification, legal advice, unrestricted red teaming, guaranteed release approval or a fully managed production service. High-stakes workflows and sensitive data require separate qualification and may need specialist involvement.
Which question is holding you back?
Describe the workflow, the decision and what you already know. We can use that context to shape a scope.